Sample records for medical model statistical
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Li, Changyang; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Yin, Yong; Dagan Feng, David
2015-01-01
Automated and general medical image segmentation can be challenging because the foreground and the background may have complicated and overlapping density distributions in medical imaging. Conventional region-based level set algorithms often assume piecewise constant or piecewise smooth for segments, which are implausible for general medical image segmentation. Furthermore, low contrast and noise make identification of the boundaries between foreground and background difficult for edge-based level set algorithms. Thus, to address these problems, we suggest a supervised variational level set segmentation model to harness the statistical region energy functional with a weighted probability approximation. Our approach models the region density distributions by using the mixture-of-mixtures Gaussian model to better approximate real intensity distributions and distinguish statistical intensity differences between foreground and background. The region-based statistical model in our algorithm can intuitively provide better performance on noisy images. We constructed a weighted probability map on graphs to incorporate spatial indications from user input with a contextual constraint based on the minimization of contextual graphs energy functional. We measured the performance of our approach on ten noisy synthetic images and 58 medical datasets with heterogeneous intensities and ill-defined boundaries and compared our technique to the Chan-Vese region-based level set model, the geodesic active contour model with distance regularization, and the random walker model. Our method consistently achieved the highest Dice similarity coefficient when compared to the other methods.
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Milic, Natasa M.; Trajkovic, Goran Z.; Bukumiric, Zoran M.; Cirkovic, Andja; Nikolic, Ivan M.; Milin, Jelena S.; Milic, Nikola V.; Savic, Marko D.; Corac, Aleksandar M.; Marinkovic, Jelena M.; Stanisavljevic, Dejana M.
2016-01-01
Background Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. Methods This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Results Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (plearning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics. PMID:26859832
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Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall
2016-01-01
Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.
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Directory of Open Access Journals (Sweden)
Natasa M Milic
Full Text Available Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face learning to further assess the potential value of web-based learning in medical statistics.This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545 the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course.Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001 and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023 with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001.This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional
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Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M
2016-01-01
Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (pstatistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.
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The Integrated Medical Model: Statistical Forecasting of Risks to Crew Health and Mission Success
Fitts, M. A.; Kerstman, E.; Butler, D. J.; Walton, M. E.; Minard, C. G.; Saile, L. G.; Toy, S.; Myers, J.
2008-01-01
The Integrated Medical Model (IMM) helps capture and use organizational knowledge across the space medicine, training, operations, engineering, and research domains. The IMM uses this domain knowledge in the context of a mission and crew profile to forecast crew health and mission success risks. The IMM is most helpful in comparing the risk of two or more mission profiles, not as a tool for predicting absolute risk. The process of building the IMM adheres to Probability Risk Assessment (PRA) techniques described in NASA Procedural Requirement (NPR) 8705.5, and uses current evidence-based information to establish a defensible position for making decisions that help ensure crew health and mission success. The IMM quantitatively describes the following input parameters: 1) medical conditions and likelihood, 2) mission duration, 3) vehicle environment, 4) crew attributes (e.g. age, sex), 5) crew activities (e.g. EVA's, Lunar excursions), 6) diagnosis and treatment protocols (e.g. medical equipment, consumables pharmaceuticals), and 7) Crew Medical Officer (CMO) training effectiveness. It is worth reiterating that the IMM uses the data sets above as inputs. Many other risk management efforts stop at determining only likelihood. The IMM is unique in that it models not only likelihood, but risk mitigations, as well as subsequent clinical outcomes based on those mitigations. Once the mathematical relationships among the above parameters are established, the IMM uses a Monte Carlo simulation technique (a random sampling of the inputs as described by their statistical distribution) to determine the probable outcomes. Because the IMM is a stochastic model (i.e. the input parameters are represented by various statistical distributions depending on the data type), when the mission is simulated 10-50,000 times with a given set of medical capabilities (risk mitigations), a prediction of the most probable outcomes can be generated. For each mission, the IMM tracks which conditions
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Statistical text classifier to detect specific type of medical incidents.
Wong, Zoie Shui-Yee; Akiyama, Masanori
2013-01-01
WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.
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Reporting Statistical Results in Medical Journals
Arifin, Wan Nor; Sarimah, Abdullah; Norsa’adah, Bachok; Najib Majdi, Yaacob; Siti-Azrin, Ab Hamid; Kamarul Imran, Musa; Aniza, Abd Aziz; Naing, Lin
2016-01-01
Statistical editors of the Malaysian Journal of Medical Sciences (MJMS) must go through many submitted manuscripts, focusing on the statistical aspect of the manuscripts. However, the editors notice myriad styles of reporting the statistical results, which are not standardised among the authors. This could be due to the lack of clear written instructions on reporting statistics in the guidelines for authors. The aim of this editorial is to briefly outline reporting methods for several important and common statistical results. It will also address a number of common mistakes made by the authors. The editorial will serve as a guideline for authors aiming to publish in the MJMS as well as in other medical journals. PMID:27904419
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2012 aerospace medical certification statistical handbook.
2013-12-01
The annual Aerospace Medical Certification Statistical Handbook reports descriptive : characteristics of all active U.S. civil aviation airmen and the aviation medical examiners (AMEs) that : perform the required medical examinations. The 2012 annual...
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2011 aerospace medical certification statistical handbook.
2013-01-01
The annual Aerospace Medical Certification Statistical Handbook reports descriptive characteristics of all active U.S. civil aviation airmen and the aviation medical examiners (AMEs) that perform the required medical examinations. The 2011 annual han...
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An introduction to medical statistics
International Nuclear Information System (INIS)
Hilgers, R.D.; Bauer, P.; Scheiber, V.; Heitmann, K.U.
2002-01-01
This textbook teaches all aspects and methods of biometrics as a field of concentration in medical education. Instrumental interpretations of the theory, concepts and terminology of medical statistics are enhanced by numerous illustrations and examples. With problems, questions and answers. (orig./CB) [de
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Medical facility statistics in Japan.
Hamajima, Nobuyuki; Sugimoto, Takuya; Hasebe, Ryo; Myat Cho, Su; Khaing, Moe; Kariya, Tetsuyoshi; Mon Saw, Yu; Yamamoto, Eiko
2017-11-01
Medical facility statistics provide essential information to policymakers, administrators, academics, and practitioners in the field of health services. In Japan, the Health Statistics Office of the Director-General for Statistics and Information Policy at the Ministry of Health, Labour and Welfare is generating these statistics. Although the statistics are widely available in both Japanese and English, the methodology described in the technical reports are primarily in Japanese, and are not fully described in English. This article aimed to describe these processes for readers in the English-speaking world. The Health Statistics Office routinely conduct two surveys called the Hospital Report and the Survey of Medical Institutions. The subjects of the former are all the hospitals and clinics with long-term care beds in Japan. It comprises a Patient Questionnaire focusing on the numbers of inpatients, admissions, discharges, and outpatients in one month, and an Employee Questionnaire, which asks about the number of employees as of October 1. The Survey of Medical Institutions consists of the Dynamic Survey, which focuses on the opening and closing of facilities every month, and the Static Survey, which focuses on staff, facilities, and services as of October 1, as well as the number of inpatients as of September 30 and the total number of outpatients during September. All hospitals, clinics, and dental clinics are requested to submit the Static Survey questionnaire every three years. These surveys are useful tools for collecting essential information, as well as providing occasions to implicitly inform facilities of the movements of government policy.
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2012 Aerospace Medical Certification Statistical Handbook
2013-12-01
2012 Aerospace Medical Certification Statistical Handbook Valerie J. Skaggs Ann I. Norris Civil Aerospace Medical Institute Federal Aviation...Certification Statistical Handbook December 2013 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Skaggs VJ, Norris AI 9...2.57 Hayfever 14,477 2.49 Asthma 12,558 2.16 Other general heart pathology (abnormal ECG, open heart surgery, etc.). Wolff-Parkinson-White syndrome
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Wu, Yazhou; Zhang, Ling; Liu, Ling; Zhang, Yanqi; Liu, Xiaoyu; Yi, Dong
2015-01-01
It is clear that the teaching of medical statistics needs to be improved, yet areas for priority are unclear as medical students' learning and application of statistics at different levels is not well known. Our goal is to assess the attitudes of medical students toward the learning and application of medical statistics, and discover their…
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Statistical problems in medical research
African Journals Online (AJOL)
STORAGESEVER
2008-12-29
Dec 29, 2008 ... medical research, there are some common problems in using statistical methodology which may result ... optimal combination of diagnostic tests for osteoporosis .... randomization used include stratification and minimize-.
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Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring.
Zhang, Yuhai; Shang, Lei; Wang, Rui; Zhao, Qinbo; Li, Chanjuan; Xu, Yongyong; Su, Haixia
2012-11-23
In medical training, statistics is considered a very difficult course to learn and teach. Current studies have found that students' attitudes toward statistics can influence their learning process. Measuring, evaluating and monitoring the changes of students' attitudes toward statistics are important. Few studies have focused on the attitudes of postgraduates, especially medical postgraduates. Our purpose was to understand current attitudes regarding statistics held by medical postgraduates and explore their effects on students' achievement. We also wanted to explore the influencing factors and the sources of these attitudes and monitor their changes after a systematic statistics course. A total of 539 medical postgraduates enrolled in a systematic statistics course completed the pre-form of the Survey of Attitudes Toward Statistics -28 scale, and 83 postgraduates were selected randomly from among them to complete the post-form scale after the course. Most medical postgraduates held positive attitudes toward statistics, but they thought statistics was a very difficult subject. The attitudes mainly came from experiences in a former statistical or mathematical class. Age, level of statistical education, research experience, specialty and mathematics basis may influence postgraduate attitudes toward statistics. There were significant positive correlations between course achievement and attitudes toward statistics. In general, student attitudes showed negative changes after completing a statistics course. The importance of student attitudes toward statistics must be recognized in medical postgraduate training. To make sure all students have a positive learning environment, statistics teachers should measure their students' attitudes and monitor their change of status during a course. Some necessary assistance should be offered for those students who develop negative attitudes.
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Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring
2012-01-01
Background In medical training, statistics is considered a very difficult course to learn and teach. Current studies have found that students’ attitudes toward statistics can influence their learning process. Measuring, evaluating and monitoring the changes of students’ attitudes toward statistics are important. Few studies have focused on the attitudes of postgraduates, especially medical postgraduates. Our purpose was to understand current attitudes regarding statistics held by medical postgraduates and explore their effects on students’ achievement. We also wanted to explore the influencing factors and the sources of these attitudes and monitor their changes after a systematic statistics course. Methods A total of 539 medical postgraduates enrolled in a systematic statistics course completed the pre-form of the Survey of Attitudes Toward Statistics −28 scale, and 83 postgraduates were selected randomly from among them to complete the post-form scale after the course. Results Most medical postgraduates held positive attitudes toward statistics, but they thought statistics was a very difficult subject. The attitudes mainly came from experiences in a former statistical or mathematical class. Age, level of statistical education, research experience, specialty and mathematics basis may influence postgraduate attitudes toward statistics. There were significant positive correlations between course achievement and attitudes toward statistics. In general, student attitudes showed negative changes after completing a statistics course. Conclusions The importance of student attitudes toward statistics must be recognized in medical postgraduate training. To make sure all students have a positive learning environment, statistics teachers should measure their students’ attitudes and monitor their change of status during a course. Some necessary assistance should be offered for those students who develop negative attitudes. PMID:23173770
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Statistical competencies for medical research learners: What is fundamental?
Enders, Felicity T; Lindsell, Christopher J; Welty, Leah J; Benn, Emma K T; Perkins, Susan M; Mayo, Matthew S; Rahbar, Mohammad H; Kidwell, Kelley M; Thurston, Sally W; Spratt, Heidi; Grambow, Steven C; Larson, Joseph; Carter, Rickey E; Pollock, Brad H; Oster, Robert A
2017-06-01
It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from 'fundamental' (necessary for all) to 'specialized' (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized. We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by 'fundamental' and 'specialized.' There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one's own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%). We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.
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Statistical Problems In Medical Research | Okeh | East African ...
African Journals Online (AJOL)
Given the main role of a general practitioner as a biostatistician, I thought it would be of interest to enumerate statistical problems in assessing methods of medical diagnosis in general terms. In conducting and reporting of medical research, there are some common problems in using statistical methodology which may result ...
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Woods and Russell, Hill, and the emergence of medical statistics.
Farewell, Vern; Johnson, Tony
2010-06-30
In 1937, Austin Bradford Hill wrote Principles of Medical Statistics (Lancet: London, 1937) that became renowned throughout the world and is widely associated with the birth of modern medical statistics. Some 6 years earlier Hilda Mary Woods and William Thomas Russell, colleagues of Hill at the London School of Hygiene and Tropical Medicine, wrote a similar book An Introduction to Medical Statistics (PS King and Son: London, 1931) that is little known today. We trace the origins of these two books from the foundations of early demography and vital statistics, and make a detailed examination of some of their chapters. It is clear that these texts mark a watershed in the history of medical statistics that demarcates the vital statistics of the nineteenth and early twentieth centuries from the modern discipline. Moreover, we consider that the book by Woods and Russell is of some importance in the development of medical statistics and we describe and acknowledge their place in the history of this discipline. (c) 2010 John Wiley & Sons, Ltd.
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Brief guidelines for methods and statistics in medical research
Ab Rahman, Jamalludin
2015-01-01
This book serves as a practical guide to methods and statistics in medical research. It includes step-by-step instructions on using SPSS software for statistical analysis, as well as relevant examples to help those readers who are new to research in health and medical fields. Simple texts and diagrams are provided to help explain the concepts covered, and print screens for the statistical steps and the SPSS outputs are provided, together with interpretations and examples of how to report on findings. Brief Guidelines for Methods and Statistics in Medical Research offers a valuable quick reference guide for healthcare students and practitioners conducting research in health related fields, written in an accessible style.
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Statistical physics of medical ultrasonic images
International Nuclear Information System (INIS)
Wagner, R.F.; Insana, M.F.; Brown, D.G.; Smith, S.W.
1987-01-01
The physical and statistical properties of backscattered signals in medical ultrasonic imaging are reviewed in terms of: 1) the radiofrequency signal; 2) the envelope (video or magnitude) signal; and 3) the density of samples in simple and in compounded images. There is a wealth of physical information in backscattered signals in medical ultrasound. This information is contained in the radiofrequency spectrum - which is not typically displayed to the viewer - as well as in the higher statistical moments of the envelope or video signal - which are not readily accessed by the human viewer of typical B-scans. This information may be extracted from the detected backscattered signals by straightforward signal processing techniques at low resolution
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He, Fu-yuan; Deng, Kai-wen; Huang, Sheng; Liu, Wen-long; Shi, Ji-lian
2013-09-01
The paper aims to elucidate and establish a new mathematic model: the total quantum statistical moment standard similarity (TQSMSS) on the base of the original total quantum statistical moment model and to illustrate the application of the model to medical theoretical research. The model was established combined with the statistical moment principle and the normal distribution probability density function properties, then validated and illustrated by the pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical method for them, and by analysis of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving the Buyanghanwu-decoction extract. The established model consists of four mainly parameters: (1) total quantum statistical moment similarity as ST, an overlapped area by two normal distribution probability density curves in conversion of the two TQSM parameters; (2) total variability as DT, a confidence limit of standard normal accumulation probability which is equal to the absolute difference value between the two normal accumulation probabilities within integration of their curve nodical; (3) total variable probability as 1-Ss, standard normal distribution probability within interval of D(T); (4) total variable probability (1-beta)alpha and (5) stable confident probability beta(1-alpha): the correct probability to make positive and negative conclusions under confident coefficient alpha. With the model, we had analyzed the TQSMS similarities of pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical methods for them were at range of 0.3852-0.9875 that illuminated different pharmacokinetic behaviors of each other; and the TQSMS similarities (ST) of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving Buyanghuanwu-decoction-extract were at range of 0.6842-0.999 2 that showed different constituents
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Growth curve models and statistical diagnostics
Pan, Jian-Xin
2002-01-01
Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.
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[The main directions of reforming the service of medical statistics in Ukraine].
Golubchykov, Mykhailo V; Orlova, Nataliia M; Bielikova, Inna V
2018-01-01
Introduction: Implementation of new methods of information support of managerial decision-making should ensure of the effective health system reform and create conditions for improving the quality of operational management, reasonable planning of medical care and increasing the efficiency of the use of system resources. Reforming of Medical Statistics Service of Ukraine should be considered only in the context of the reform of the entire health system. The aim: This work is an analysis of the current situation and justification of the main directions of reforming of Medical Statistics Service of Ukraine. Material and methods: In the work is used a range of methods: content analysis, bibliosemantic, systematic approach. The information base of the research became: WHO strategic and program documents, data of the Medical Statistics Center of the Ministry of Health of Ukraine. Review: The Medical Statistics Service of Ukraine has a completed and effective structure, headed by the State Institution "Medical Statistics Center of the Ministry of Health of Ukraine." This institution reports on behalf of the Ministry of Health of Ukraine to the State Statistical Service of Ukraine, the WHO European Office and other international organizations. An analysis of the current situation showed that to achieve this goal it is necessary: to improve the system of statistical indicators for an adequate assessment of the performance of health institutions, including in the economic aspect; creation of a developed medical and statistical base of administrative territories; change of existing technologies for the formation of information resources; strengthening the material-technical base of the structural units of Medical Statistics Service; improvement of the system of training and retraining of personnel for the service of medical statistics; development of international cooperation in the field of methodology and practice of medical statistics, implementation of internationally
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An 'electronic' extramural course in epidemiology and medical statistics.
Ostbye, T
1989-03-01
This article describes an extramural university course in epidemiology and medical statistics taught using a computer conferencing system, microcomputers and data communications. Computer conferencing was shown to be a powerful, yet quite easily mastered, vehicle for distance education. It allows health personnel unable to attend regular classes due to geographical or time constraints, to take part in an interactive learning environment at low cost. This overcomes part of the intellectual and social isolation associated with traditional correspondence courses. Teaching of epidemiology and medical statistics is well suited to computer conferencing, even if the asynchronicity of the medium makes discussion of the most complex statistical concepts a little cumbersome. Computer conferencing may also prove to be a useful tool for teaching other medical and health related subjects.
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Review of the Statistical Techniques in Medical Sciences | Okeh ...
African Journals Online (AJOL)
... medical researcher in selecting the appropriate statistical techniques. Of course, all statistical techniques have certain underlying assumptions, which must be checked before the technique is applied. Keywords: Variable, Prospective Studies, Retrospective Studies, Statistical significance. Bio-Research Vol. 6 (1) 2008: pp.
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Directory of Open Access Journals (Sweden)
Dejana Stanisavljevic
Full Text Available BACKGROUND: Medical statistics has become important and relevant for future doctors, enabling them to practice evidence based medicine. Recent studies report that students' attitudes towards statistics play an important role in their statistics achievements. The aim of the study was to test the psychometric properties of the Serbian version of the Survey of Attitudes Towards Statistics (SATS in order to acquire a valid instrument to measure attitudes inside the Serbian educational context. METHODS: The validation study was performed on a cohort of 417 medical students who were enrolled in an obligatory introductory statistics course. The SATS adaptation was based on an internationally accepted methodology for translation and cultural adaptation. Psychometric properties of the Serbian version of the SATS were analyzed through the examination of factorial structure and internal consistency. RESULTS: Most medical students held positive attitudes towards statistics. The average total SATS score was above neutral (4.3±0.8, and varied from 1.9 to 6.2. Confirmatory factor analysis validated the six-factor structure of the questionnaire (Affect, Cognitive Competence, Value, Difficulty, Interest and Effort. Values for fit indices TLI (0.940 and CFI (0.961 were above the cut-off of ≥0.90. The RMSEA value of 0.064 (0.051-0.078 was below the suggested value of ≤0.08. Cronbach's alpha of the entire scale was 0.90, indicating scale reliability. In a multivariate regression model, self-rating of ability in mathematics and current grade point average were significantly associated with the total SATS score after adjusting for age and gender. CONCLUSION: Present study provided the evidence for the appropriate metric properties of the Serbian version of SATS. Confirmatory factor analysis validated the six-factor structure of the scale. The SATS might be reliable and a valid instrument for identifying medical students' attitudes towards statistics in the
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Stanisavljevic, Dejana; Trajkovic, Goran; Marinkovic, Jelena; Bukumiric, Zoran; Cirkovic, Andja; Milic, Natasa
2014-01-01
Medical statistics has become important and relevant for future doctors, enabling them to practice evidence based medicine. Recent studies report that students' attitudes towards statistics play an important role in their statistics achievements. The aim of the study was to test the psychometric properties of the Serbian version of the Survey of Attitudes Towards Statistics (SATS) in order to acquire a valid instrument to measure attitudes inside the Serbian educational context. The validation study was performed on a cohort of 417 medical students who were enrolled in an obligatory introductory statistics course. The SATS adaptation was based on an internationally accepted methodology for translation and cultural adaptation. Psychometric properties of the Serbian version of the SATS were analyzed through the examination of factorial structure and internal consistency. Most medical students held positive attitudes towards statistics. The average total SATS score was above neutral (4.3±0.8), and varied from 1.9 to 6.2. Confirmatory factor analysis validated the six-factor structure of the questionnaire (Affect, Cognitive Competence, Value, Difficulty, Interest and Effort). Values for fit indices TLI (0.940) and CFI (0.961) were above the cut-off of ≥0.90. The RMSEA value of 0.064 (0.051-0.078) was below the suggested value of ≤0.08. Cronbach's alpha of the entire scale was 0.90, indicating scale reliability. In a multivariate regression model, self-rating of ability in mathematics and current grade point average were significantly associated with the total SATS score after adjusting for age and gender. Present study provided the evidence for the appropriate metric properties of the Serbian version of SATS. Confirmatory factor analysis validated the six-factor structure of the scale. The SATS might be reliable and a valid instrument for identifying medical students' attitudes towards statistics in the Serbian educational context.
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Statistics in the medical sciences - the long Germany road to there
Directory of Open Access Journals (Sweden)
Weiß, Christel
2005-06-01
Full Text Available This contribution aims at tracing the development of statistical methods in medical science. Statistical methodology in medical research was first implemented in England in the age of the Enlightenment during the 18th century. As this approach stood in a clear opposition to the conventional medical practice directed in a rather authoritarian manner, this research field had to overcome a lot of difficulties. Nowadays, there is a widespread consensus that medical research is hardly possible without profound knowledge and application of statistical methods. Nevertheless, it took an extremely long time until the end of the 20th century, before this methodology was taken notice of and became appreciated. In order to better understand this long process, a brief summary of the development of statistics beginning from the Ancient Times is presented. It is shown how medical progress evolved parallel to the advancing mathematical understanding. A focus is put on the influence of the latter on medical sciences. Moreover, the special case of Germany in this aspect is analysed.
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Statistical behavior of high doses in medical radiodiagnosis
International Nuclear Information System (INIS)
Barboza, Adriana Elisa
2014-01-01
This work has as main purpose statistically estimating occupational exposure in medical diagnostic radiology in cases of high doses recorded in 2011 at national level. For statistical survey of this study, doses of 372 IOE's diagnostic radiology in different Brazilian states were evaluated. Data were extracted from the work of monograph (Research Methodology Of High Doses In Medical Radiodiagnostic) that contains the database's information Sector Management doses of IRD/CNEN-RJ, Brazil. The identification of these states allows the Sanitary Surveillance (VISA) responsible, becomes aware of events and work with programs to reduce these events. (author)
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Complex Data Modeling and Computationally Intensive Statistical Methods
Mantovan, Pietro
2010-01-01
The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici
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Certification of medical librarians, 1949--1977 statistical analysis.
Schmidt, D
1979-01-01
The Medical Library Association's Code for Training and Certification of Medical Librarians was in effect from 1949 to August 1977, a period during which 3,216 individuals were certified. Statistics on each type of certificate granted each year are provided. Because 54.5% of those granted certification were awarded it in the last three-year, two-month period of the code's existence, these applications are reviewed in greater detail. Statistics on each type of certificate granted each year are provided. Because 54.5% of those granted certification were awarded it in the last three-year, two-month period of the code's existence, these applications are reviewed in greater detail. Statistics on MLA membership, sex, residence, library school, and method of meeting requirements are detailed. Questions relating to certification under the code now in existence are raised.
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Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
DEFF Research Database (Denmark)
Ganz, Melanie; Nielsen, Mads; Brandt, Sami
2010-01-01
We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning ...
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WE-A-201-02: Modern Statistical Modeling
Energy Technology Data Exchange (ETDEWEB)
Niemierko, A.
2016-06-15
Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear
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WE-A-201-02: Modern Statistical Modeling
International Nuclear Information System (INIS)
Niemierko, A.
2016-01-01
Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear
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Visualization of the variability of 3D statistical shape models by animation.
Lamecker, Hans; Seebass, Martin; Lange, Thomas; Hege, Hans-Christian; Deuflhard, Peter
2004-01-01
Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.
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Are medical articles highlighting detailed statistics more cited?
Directory of Open Access Journals (Sweden)
Mike Thelwall
2015-06-01
Full Text Available When conducting a literature review, it is natural to search for articles and read their abstracts in order to select papers to read fully. Hence, informative abstracts are important to ensure that research is read. The description of a paper's methods may help to give confidence that a study is of high quality. This article assesses whether medical articles that mention three statistical methods, each of which is arguably indicative of a more detailed statistical analysis than average, are more highly cited. The results show that medical articles mentioning Bonferroni corrections, bootstrapping and effect size tend to be 7%, 8% and 15% more highly ranked for citations than average, respectively. Although this is consistent with the hypothesis that mentioning more detailed statistical techniques generate more highly cited research, these techniques may also tend to be used in more highly cited areas of Medicine.
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Roles of Medical Record and Statistic Staff on Research at the Tawanchai Center.
Pattaranit, Rumpan; Chantachum, Vasana; Lekboonyasin, Orathai; Pradubwong, Suteera
2015-08-01
The medical record and statistic staffs play a crucial role behind the achievements of treatment and research of physicians, nurses and other health care professionals. The medical record and statistic staff are in charge of keeping patient medical records; creating databases; presenting information; sorting patient's information; providing patient medical records and related information for various medical teams and researchers; Besides, the medical record and statistic staff have collaboration with the Center of Cleft Lip-Palate, Khon Kaen University in association with the Tawanchai Project. The Tawanchai Center is an organization, involving multidisciplinary team which aims to continuing provide care for patients with cleft lip and palate and craniofacial deformities who need a long term of treatment since newborns until the age of 19 years. With support and encouragement from the Tawanchai team, the medical record and statistic staff have involved in research under the Tawanchai Centre since then and produced a number of publications locally and internationally.
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Peculiarities of Teaching Medical Informatics and Statistics
Directory of Open Access Journals (Sweden)
Sergey Glushkov
2017-05-01
Full Text Available The article reviews features of teaching Medical Informatics and Statistics. The course is referred to the disciplines of Mathematical and Natural sciences. The course is provided in all the faculties of I. M. Sechenov First Moscow State Medical University. For students of Preventive Medicine Department the time frame allotted for studying the course is significantly larger than for similar course provided at other faculties. To improve the teaching methodology of the discipline an analysis of the curriculum has been carried out, attendance and students’ performance statistics have been summarized. As a result, the main goals and objectives have been identified. Besides, general educational functions and the contribution to the solution of problems of education, students’ upbringing and development have been revealed; two stages of teaching have been presented. Recommendations referred to the newest methodological development aimed at improving the quality of teaching the discipline are provided. The ways of improving the methods and organizational forms of education are outlined.
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The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks
Keenan, Alexandra; Young, Millennia; Saile, Lynn; Boley, Lynn; Walton, Marlei; Kerstman, Eric; Shah, Ronak; Goodenow, Debra A.; Myers, Jerry G., Jr.
2015-01-01
The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting
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[''R"--project for statistical computing
DEFF Research Database (Denmark)
Dessau, R.B.; Pipper, Christian Bressen
2008-01-01
An introduction to the R project for statistical computing (www.R-project.org) is presented. The main topics are: 1. To make the professional community aware of "R" as a potent and free software for graphical and statistical analysis of medical data; 2. Simple well-known statistical tests are fai...... are fairly easy to perform in R, but more complex modelling requires programming skills; 3. R is seen as a tool for teaching statistics and implementing complex modelling of medical data among medical professionals Udgivelsesdato: 2008/1/28......An introduction to the R project for statistical computing (www.R-project.org) is presented. The main topics are: 1. To make the professional community aware of "R" as a potent and free software for graphical and statistical analysis of medical data; 2. Simple well-known statistical tests...
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Keenan, Alexandra; Young, Millennia; Saile, Lynn; Boley, Lynn; Walton, Marlei; Kerstman, Eric; Shah, Ronak; Goodenow, Debra A.; Myers, Jerry G.
2015-01-01
The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting
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Research design and statistical methods in Indian medical journals: a retrospective survey.
Hassan, Shabbeer; Yellur, Rajashree; Subramani, Pooventhan; Adiga, Poornima; Gokhale, Manoj; Iyer, Manasa S; Mayya, Shreemathi S
2015-01-01
Good quality medical research generally requires not only an expertise in the chosen medical field of interest but also a sound knowledge of statistical methodology. The number of medical research articles which have been published in Indian medical journals has increased quite substantially in the past decade. The aim of this study was to collate all evidence on study design quality and statistical analyses used in selected leading Indian medical journals. Ten (10) leading Indian medical journals were selected based on impact factors and all original research articles published in 2003 (N = 588) and 2013 (N = 774) were categorized and reviewed. A validated checklist on study design, statistical analyses, results presentation, and interpretation was used for review and evaluation of the articles. Main outcomes considered in the present study were - study design types and their frequencies, error/defects proportion in study design, statistical analyses, and implementation of CONSORT checklist in RCT (randomized clinical trials). From 2003 to 2013: The proportion of erroneous statistical analyses did not decrease (χ2=0.592, Φ=0.027, p=0.4418), 25% (80/320) in 2003 compared to 22.6% (111/490) in 2013. Compared with 2003, significant improvement was seen in 2013; the proportion of papers using statistical tests increased significantly (χ2=26.96, Φ=0.16, pdesign decreased significantly (χ2=16.783, Φ=0.12 pdesigns has remained very low (7.3%, 43/588) with majority showing some errors (41 papers, 95.3%). Majority of the published studies were retrospective in nature both in 2003 [79.1% (465/588)] and in 2013 [78.2% (605/774)]. Major decreases in error proportions were observed in both results presentation (χ2=24.477, Φ=0.17, presearch seems to have made no major progress regarding using correct statistical analyses, but error/defects in study designs have decreased significantly. Randomized clinical trials are quite rarely published and have high proportion of
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Paradigms and pragmatism: approaches to medical statistics.
Healy, M J
2000-01-01
Until recently, the dominant philosophy of science was that due to Karl Popper, with its doctrine that the proper task of science was the formulation of hypotheses followed by attempts at refuting them. In spite of the close analogy with significance testing, these ideas do not fit well with the practice of medical statistics. The same can be said of the later philosophy of Thomas Kuhn, who maintains that science proceeds by way of revolutionary upheavals separated by periods of relatively pedestrian research which are governed by what Kuhn refers to as paradigms. Through there have been paradigm shifts in the history of statistics, a degree of continuity can also be discerned. A current paradigm shift is embodied in the spread of Bayesian ideas. It may be that a future paradigm will emphasise the pragmatic approach to statistics that is associated with the name of Daniel Schwartz.
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Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong
2015-01-01
Background Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. Objectives This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. Methods We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. Results There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. Conclusion The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent
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International Nuclear Information System (INIS)
Yamaoka, Naoto; Watanabe, Wataru; Hontani, Hidekata
2010-01-01
Most of the time when we construct statistical point cloud model, we need to calculate the corresponding points. Constructed statistical model will not be the same if we use different types of method to calculate the corresponding points. This article proposes the effect to statistical model of human organ made by different types of method to calculate the corresponding points. We validated the performance of statistical model by registering a surface of an organ in a 3D medical image. We compare two methods to calculate corresponding points. The first, the 'Generalized Multi-Dimensional Scaling (GMDS)', determines the corresponding points by the shapes of two curved surfaces. The second approach, the 'Entropy-based Particle system', chooses corresponding points by calculating a number of curved surfaces statistically. By these methods we construct the statistical models and using these models we conducted registration with the medical image. For the estimation, we use non-parametric belief propagation and this method estimates not only the position of the organ but also the probability density of the organ position. We evaluate how the two different types of method that calculates corresponding points affects the statistical model by change in probability density of each points. (author)
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Sampling, Probability Models and Statistical Reasoning Statistical
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Sampling, Probability Models and Statistical Reasoning Statistical Inference. Mohan Delampady V R Padmawar. General Article Volume 1 Issue 5 May 1996 pp 49-58 ...
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Directory of Open Access Journals (Sweden)
Natasa M Milic
Full Text Available The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students' attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students.A validated Serbian Survey of Attitudes Towards Statistics (SATS-36 questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models.SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95. The positive correlation found between the Cognitive Competence score and students' achievement (r = 0.41; p<0.001, was also shown in the meta-analysis (r = 0.37; 95% CI 0.32-0.41.Students' subjective attitudes regarding Cognitive Competence at the beginning of the biostatistics course, which were
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Milic, Natasa M; Masic, Srdjan; Milin-Lazovic, Jelena; Trajkovic, Goran; Bukumiric, Zoran; Savic, Marko; Milic, Nikola V; Cirkovic, Andja; Gajic, Milan; Kostic, Mirjana; Ilic, Aleksandra; Stanisavljevic, Dejana
2016-01-01
The scientific community increasingly is recognizing the need to bolster standards of data analysis given the widespread concern that basic mistakes in data analysis are contributing to the irreproducibility of many published research findings. The aim of this study was to investigate students' attitudes towards statistics within a multi-site medical educational context, monitor their changes and impact on student achievement. In addition, we performed a systematic review to better support our future pedagogical decisions in teaching applied statistics to medical students. A validated Serbian Survey of Attitudes Towards Statistics (SATS-36) questionnaire was administered to medical students attending obligatory introductory courses in biostatistics from three medical universities in the Western Balkans. A systematic review of peer-reviewed publications was performed through searches of Scopus, Web of Science, Science Direct, Medline, and APA databases through 1994. A meta-analysis was performed for the correlation coefficients between SATS component scores and statistics achievement. Pooled estimates were calculated using random effects models. SATS-36 was completed by 461 medical students. Most of the students held positive attitudes towards statistics. Ability in mathematics and grade point average were associated in a multivariate regression model with the Cognitive Competence score, after adjusting for age, gender and computer ability. The results of 90 paired data showed that Affect, Cognitive Competence, and Effort scores demonstrated significant positive changes. The Cognitive Competence score showed the largest increase (M = 0.48, SD = 0.95). The positive correlation found between the Cognitive Competence score and students' achievement (r = 0.41; p<0.001), was also shown in the meta-analysis (r = 0.37; 95% CI 0.32-0.41). Students' subjective attitudes regarding Cognitive Competence at the beginning of the biostatistics course, which were directly linked to
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Statistical Model of Extreme Shear
DEFF Research Database (Denmark)
Larsen, Gunner Chr.; Hansen, Kurt Schaldemose
2004-01-01
In order to continue cost-optimisation of modern large wind turbines, it is important to continously increase the knowledge on wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describe the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of high-sampled full-scale time series measurements...... are consistent, given the inevitabel uncertainties associated with model as well as with the extreme value data analysis. Keywords: Statistical model, extreme wind conditions, statistical analysis, turbulence, wind loading, statistical analysis, turbulence, wind loading, wind shear, wind turbines....
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Medical Statistics – Mathematics or Oracle? Farewell Lecture
Directory of Open Access Journals (Sweden)
Gaus, Wilhelm
2005-06-01
Full Text Available Certainty is rare in medicine. This is a direct consequence of the individuality of each and every human being and the reason why we need medical statistics. However, statistics have their pitfalls, too. Fig. 1 shows that the suicide rate peaks in youth, while in Fig. 2 the rate is highest in midlife and Fig. 3 in old age. Which of these contradictory messages is right? After an introduction to the principles of statistical testing, this lecture examines the probability with which statistical test results are correct. For this purpose the level of significance and the power of the test are compared with the sensitivity and specificity of a diagnostic procedure. The probability of obtaining correct statistical test results is the same as that for the positive and negative correctness of a diagnostic procedure and therefore depends on prevalence. The focus then shifts to the problem of multiple statistical testing. The lecture demonstrates that for each data set of reasonable size at least one test result proves to be significant - even if the data set is produced by a random number generator. It is extremely important that a hypothesis is generated independently from the data used for its testing. These considerations enable us to understand the gradation of "lame excuses, lies and statistics" and the difference between pure truth and the full truth. Finally, two historical oracles are cited.
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Exclusion statistics and integrable models
International Nuclear Information System (INIS)
Mashkevich, S.
1998-01-01
The definition of exclusion statistics that was given by Haldane admits a 'statistical interaction' between distinguishable particles (multispecies statistics). For such statistics, thermodynamic quantities can be evaluated exactly; explicit expressions are presented here for cluster coefficients. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models of the Calogero-Sutherland type. The interesting questions of generalizing this correspondence to the higher-dimensional and the multispecies cases remain essentially open; however, our results provide some hints as to searches for the models in question
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Statistical Modeling of the Trends Concerning the Number of Hospitals and Medical Centres in Romania
Directory of Open Access Journals (Sweden)
Gabriela OPAIT
2017-04-01
Full Text Available This study reveals the technique for to achive the shapes of the mathematical models which put in evidence the distributions of the values concerning the number of Hospitals, respectively Medical Centres, in our country, in the time horizon 2005-2014. In the same time, we can to observe the algorithm applied for to construct forecasts about the evolutions regarding the number of Hospitals and Medical Centres in Romania.
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Gilkey, Kelly M.; Myers, Jerry G.; McRae, Michael P.; Griffin, Elise A.; Kallrui, Aditya S.
2012-01-01
The Exploration Medical Capability project is creating a catalog of risk assessments using the Integrated Medical Model (IMM). The IMM is a software-based system intended to assist mission planners in preparing for spaceflight missions by helping them to make informed decisions about medical preparations and supplies needed for combating and treating various medical events using Probabilistic Risk Assessment. The objective is to use statistical analyses to inform the IMM decision tool with estimated probabilities of medical events occurring during an exploration mission. Because data regarding astronaut health are limited, Bayesian statistical analysis is used. Bayesian inference combines prior knowledge, such as data from the general U.S. population, the U.S. Submarine Force, or the analog astronaut population located at the NASA Johnson Space Center, with observed data for the medical condition of interest. The posterior results reflect the best evidence for specific medical events occurring in flight. Bayes theorem provides a formal mechanism for combining available observed data with data from similar studies to support the quantification process. The IMM team performed Bayesian updates on the following medical events: angina, appendicitis, atrial fibrillation, atrial flutter, dental abscess, dental caries, dental periodontal disease, gallstone disease, herpes zoster, renal stones, seizure, and stroke.
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Directory of Open Access Journals (Sweden)
Hamid Reza Marateb
2014-01-01
Full Text Available Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal-variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD. Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.
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Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario
2014-01-01
Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565
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Statistical modelling with quantile functions
Gilchrist, Warren
2000-01-01
Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of quantiles in their work. Until now, however, no one put all the ideas together to form what turns out to be a general approach to statistics.Statistical Modelling with Quantile Functions does just that. It systematically examines the entire process of statistical modelling, starting with using the quantile function to define continuous distributions. The author shows that by using this approach, it becomes possible to develop complex distributional models from simple components. A modelling kit can be developed that applies to the whole model - deterministic and stochastic components - and this kit operates by adding, multiplying, and transforming distributions rather than data.Statistical Modelling with Quantile Functions adds a new dimension to the practice of stati...
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A Statistical Programme Assignment Model
DEFF Research Database (Denmark)
Rosholm, Michael; Staghøj, Jonas; Svarer, Michael
When treatment effects of active labour market programmes are heterogeneous in an observable way across the population, the allocation of the unemployed into different programmes becomes a particularly important issue. In this paper, we present a statistical model designed to improve the present...... duration of unemployment spells may result if a statistical programme assignment model is introduced. We discuss several issues regarding the plementation of such a system, especially the interplay between the statistical model and case workers....
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Dyscalculia, dyslexia, and medical students' needs for learning and using statistics.
MacDougall, Margaret
2009-02-07
Much has been written on the learning needs of dyslexic and dyscalculic students in primary and early secondary education. However, it is not clear that the necessary disability support staff and specialist literature are available to ensure that these needs are being adequately met within the context of learning statistics and general quantitative skills in the self-directed learning environments encountered in higher education. This commentary draws attention to dyslexia and dyscalculia as two potentially unrecognized conditions among undergraduate medical students and in turn, highlights key developments from recent literature in the diagnosis of these conditions. With a view to assisting medical educators meet the needs of dyscalculic learners and the more varied needs of dyslexic learners, a comprehensive list of suggestions is provided as to how learning resources can be designed from the outset to be more inclusive. A hitherto neglected area for future research is also identified through a call for a thorough investigation of the meaning of statistical literacy within the context of the undergraduate medical curriculum.
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Dyscalculia, Dyslexia, and Medical Students’ Needs for Learning and Using Statistics
MacDougall, Margaret
2009-01-01
Much has been written on the learning needs of dyslexic and dyscalculic students in primary and early secondary education. However, it is not clear that the necessary disability support staff and specialist literature are available to ensure that these needs are being adequately met within the context of learning statistics and general quantitative skills in the self-directed learning environments encountered in higher education. This commentary draws attention to dyslexia and dyscalculia as two potentially unrecognized conditions among undergraduate medical students and in turn, highlights key developments from recent literature in the diagnosis of these conditions. With a view to assisting medical educators meet the needs of dyscalculic learners and the more varied needs of dyslexic learners, a comprehensive list of suggestions is provided as to how learning resources can be designed from the outset to be more inclusive. A hitherto neglected area for future research is also identified through a call for a thorough investigation of the meaning of statistical literacy within the context of the undergraduate medical curriculum. PMID:20165516
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Statistical model for prediction of hearing loss in patients receiving cisplatin chemotherapy.
Johnson, Andrew; Tarima, Sergey; Wong, Stuart; Friedland, David R; Runge, Christina L
2013-03-01
This statistical model might be used to predict cisplatin-induced hearing loss, particularly in patients undergoing concomitant radiotherapy. To create a statistical model based on pretreatment hearing thresholds to provide an individual probability for hearing loss from cisplatin therapy and, secondarily, to investigate the use of hearing classification schemes as predictive tools for hearing loss. Retrospective case-control study. Tertiary care medical center. A total of 112 subjects receiving chemotherapy and audiometric evaluation were evaluated for the study. Of these subjects, 31 met inclusion criteria for analysis. The primary outcome measurement was a statistical model providing the probability of hearing loss following the use of cisplatin chemotherapy. Fifteen of the 31 subjects had significant hearing loss following cisplatin chemotherapy. American Academy of Otolaryngology-Head and Neck Society and Gardner-Robertson hearing classification schemes revealed little change in hearing grades between pretreatment and posttreatment evaluations for subjects with or without hearing loss. The Chang hearing classification scheme could effectively be used as a predictive tool in determining hearing loss with a sensitivity of 73.33%. Pretreatment hearing thresholds were used to generate a statistical model, based on quadratic approximation, to predict hearing loss (C statistic = 0.842, cross-validated = 0.835). The validity of the model improved when only subjects who received concurrent head and neck irradiation were included in the analysis (C statistic = 0.91). A calculated cutoff of 0.45 for predicted probability has a cross-validated sensitivity and specificity of 80%. Pretreatment hearing thresholds can be used as a predictive tool for cisplatin-induced hearing loss, particularly with concomitant radiotherapy.
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Diffeomorphic Statistical Deformation Models
DEFF Research Database (Denmark)
Hansen, Michael Sass; Hansen, Mads/Fogtman; Larsen, Rasmus
2007-01-01
In this paper we present a new method for constructing diffeomorphic statistical deformation models in arbitrary dimensional images with a nonlinear generative model and a linear parameter space. Our deformation model is a modified version of the diffeomorphic model introduced by Cootes et al....... The modifications ensure that no boundary restriction has to be enforced on the parameter space to prevent folds or tears in the deformation field. For straightforward statistical analysis, principal component analysis and sparse methods, we assume that the parameters for a class of deformations lie on a linear...... with ground truth in form of manual expert annotations, and compared to Cootes's model. We anticipate applications in unconstrained diffeomorphic synthesis of images, e.g. for tracking, segmentation, registration or classification purposes....
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Directory of Open Access Journals (Sweden)
Xiaoliang Jiang
2014-01-01
Full Text Available This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances. Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are simultaneously achieved by minimizing the level set formulation. Experimental results demonstrate desirable performance of the proposed method for different medical images with weak boundaries and noise.
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How Medical Statistics has been established at the University of Freiburg: a historical perspective
Directory of Open Access Journals (Sweden)
Schumacher, Martin
2005-06-01
Full Text Available This contribution gives an outline on the reasons why the Faculty of Medicine at the University of Freiburg established an Institute of Medical Statistics and Documentation about fourty years ago as one of the first in Germany. It will be shown that the Professor of Medical Microbiology and Hygiene at that time initiated and promoted this development being himself motivated by the successful implementation of a vaccine against poliomyelitis through rigorous design, conduct and statistical analysis of a large scale field trial.
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Statistical modeling for degradation data
Lio, Yuhlong; Ng, Hon; Tsai, Tzong-Ru
2017-01-01
This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products’ reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.
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Exclusion statistics and integrable models
International Nuclear Information System (INIS)
Mashkevich, S.
1998-01-01
The definition of exclusion statistics, as given by Haldane, allows for a statistical interaction between distinguishable particles (multi-species statistics). The thermodynamic quantities for such statistics ca be evaluated exactly. The explicit expressions for the cluster coefficients are presented. Furthermore, single-species exclusion statistics is realized in one-dimensional integrable models. The interesting questions of generalizing this correspondence onto the higher-dimensional and the multi-species cases remain essentially open
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Medical school attrition-beyond the statistics a ten year retrospective study.
Maher, Bridget M; Hynes, Helen; Sweeney, Catherine; Khashan, Ali S; O'Rourke, Margaret; Doran, Kieran; Harris, Anne; Flynn, Siun O'
2013-01-31
Medical school attrition is important--securing a place in medical school is difficult and a high attrition rate can affect the academic reputation of a medical school and staff morale. More important, however, are the personal consequences of dropout for the student. The aims of our study were to examine factors associated with attrition over a ten-year period (2001-2011) and to study the personal effects of dropout on individual students. The study included quantitative analysis of completed cohorts and qualitative analysis of ten-year data. Data were collected from individual student files, examination and admission records, exit interviews and staff interviews. Statistical analysis was carried out on five successive completed cohorts. Qualitative data from student files was transcribed and independently analysed by three authors. Data was coded and categorized and key themes were identified. Overall attrition rate was 5.7% (45/779) in 6 completed cohorts when students who transferred to other medical courses were excluded. Students from Kuwait and United Arab Emirates had the highest dropout rate (RR = 5.70, 95% Confidence Intervals 2.65 to 12.27;p psychological morbidity in 40% (higher than other studies). Qualitative analysis revealed recurrent themes of isolation, failure, and despair. Student Welfare services were only accessed by one-third of dropout students. While dropout is often multifactorial, certain red flag signals may alert us to risk of dropout including non-EU origin, academic struggling, absenteeism, social isolation, depression and leave of absence. Psychological morbidity amongst dropout students is high and Student Welfare services should be actively promoted. Absenteeism should prompt early intervention. Behind every dropout statistic lies a personal story. All medical schools have a duty of care to support students who leave the medical programme.
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An introduction to medical statistics; Einfuehrung in die Medizinische Statistik
Energy Technology Data Exchange (ETDEWEB)
Hilgers, R.D. [Technische Hochschule Aachen (Germany). Inst. fuer Medizinische Statistik; Bauer, P.; Scheiber, V. [Wien Univ. (Austria). Inst. fuer Medizinische Statistik; Heitmann, K.U. [Koeln Univ. (Germany). Inst. fuer Medizinische Statistik, Informatik und Epidemiologie
2002-07-01
This textbook teaches all aspects and methods of biometrics as a field of concentration in medical education. Instrumental interpretations of the theory, concepts and terminology of medical statistics are enhanced by numerous illustrations and examples. With problems, questions and answers. (orig./CB) [German] Das Buch fuehrt systematisch und umfassend in die gaengigen statistischen Methoden in der Medizin und deren Terminologie ein. Es entspricht sowohl dem aktuellen wie auch dem zukuenftigen Gegenstandskatalog fuer Biometrie in der Ausbildung fuer Mediziner. Die Darstellung der theoretischen Konzepte wird durch zahlreiche Abbildungen und medizinische Beispiele veranschaulicht. MC-orientierte Uebungsaufgaben mit Loesungen helfen dem Leser das erlernte Wissen zu vertiefen. (orig.)
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Analytical model of SiPM time resolution and order statistics with crosstalk
International Nuclear Information System (INIS)
Vinogradov, S.
2015-01-01
Time resolution is the most important parameter of photon detectors in a wide range of time-of-flight and time correlation applications within the areas of high energy physics, medical imaging, and others. Silicon photomultipliers (SiPM) have been initially recognized as perfect photon-number-resolving detectors; now they also provide outstanding results in the scintillator timing resolution. However, crosstalk and afterpulsing introduce false secondary non-Poissonian events, and SiPM time resolution models are experiencing significant difficulties with that. This study presents an attempt to develop an analytical model of the timing resolution of an SiPM taking into account statistics of secondary events resulting from a crosstalk. Two approaches have been utilized to derive an analytical expression for time resolution: the first one based on statistics of independent identically distributed detection event times and the second one based on order statistics of these times. The first approach is found to be more straightforward and “analytical-friendly” to model analog SiPMs. Comparisons of coincidence resolving times predicted by the model with the known experimental results from a LYSO:Ce scintillator and a Hamamatsu MPPC are presented
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Analytical model of SiPM time resolution and order statistics with crosstalk
Energy Technology Data Exchange (ETDEWEB)
Vinogradov, S., E-mail: Sergey.Vinogradov@liverpool.ac.uk [University of Liverpool and Cockcroft Institute, Sci-Tech Daresbury, Keckwick Lane, Warrington WA4 4AD (United Kingdom); P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 119991 Leninskiy Prospekt 53, Moscow (Russian Federation)
2015-07-01
Time resolution is the most important parameter of photon detectors in a wide range of time-of-flight and time correlation applications within the areas of high energy physics, medical imaging, and others. Silicon photomultipliers (SiPM) have been initially recognized as perfect photon-number-resolving detectors; now they also provide outstanding results in the scintillator timing resolution. However, crosstalk and afterpulsing introduce false secondary non-Poissonian events, and SiPM time resolution models are experiencing significant difficulties with that. This study presents an attempt to develop an analytical model of the timing resolution of an SiPM taking into account statistics of secondary events resulting from a crosstalk. Two approaches have been utilized to derive an analytical expression for time resolution: the first one based on statistics of independent identically distributed detection event times and the second one based on order statistics of these times. The first approach is found to be more straightforward and “analytical-friendly” to model analog SiPMs. Comparisons of coincidence resolving times predicted by the model with the known experimental results from a LYSO:Ce scintillator and a Hamamatsu MPPC are presented.
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Bruse, Jan L; McLeod, Kristin; Biglino, Giovanni; Ntsinjana, Hopewell N; Capelli, Claudio; Hsia, Tain-Yen; Sermesant, Maxime; Pennec, Xavier; Taylor, Andrew M; Schievano, Silvia
2016-05-31
Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. The suggested method has the potential to discover
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Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
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Smooth extrapolation of unknown anatomy via statistical shape models
Grupp, R. B.; Chiang, H.; Otake, Y.; Murphy, R. J.; Gordon, C. R.; Armand, M.; Taylor, R. H.
2015-03-01
Several methods to perform extrapolation of unknown anatomy were evaluated. The primary application is to enhance surgical procedures that may use partial medical images or medical images of incomplete anatomy. Le Fort-based, face-jaw-teeth transplant is one such procedure. From CT data of 36 skulls and 21 mandibles separate Statistical Shape Models of the anatomical surfaces were created. Using the Statistical Shape Models, incomplete surfaces were projected to obtain complete surface estimates. The surface estimates exhibit non-zero error in regions where the true surface is known; it is desirable to keep the true surface and seamlessly merge the estimated unknown surface. Existing extrapolation techniques produce non-smooth transitions from the true surface to the estimated surface, resulting in additional error and a less aesthetically pleasing result. The three extrapolation techniques evaluated were: copying and pasting of the surface estimate (non-smooth baseline), a feathering between the patient surface and surface estimate, and an estimate generated via a Thin Plate Spline trained from displacements between the surface estimate and corresponding vertices of the known patient surface. Feathering and Thin Plate Spline approaches both yielded smooth transitions. However, feathering corrupted known vertex values. Leave-one-out analyses were conducted, with 5% to 50% of known anatomy removed from the left-out patient and estimated via the proposed approaches. The Thin Plate Spline approach yielded smaller errors than the other two approaches, with an average vertex error improvement of 1.46 mm and 1.38 mm for the skull and mandible respectively, over the baseline approach.
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Classical model of intermediate statistics
International Nuclear Information System (INIS)
Kaniadakis, G.
1994-01-01
In this work we present a classical kinetic model of intermediate statistics. In the case of Brownian particles we show that the Fermi-Dirac (FD) and Bose-Einstein (BE) distributions can be obtained, just as the Maxwell-Boltzmann (MD) distribution, as steady states of a classical kinetic equation that intrinsically takes into account an exclusion-inclusion principle. In our model the intermediate statistics are obtained as steady states of a system of coupled nonlinear kinetic equations, where the coupling constants are the transmutational potentials η κκ' . We show that, besides the FD-BE intermediate statistics extensively studied from the quantum point of view, we can also study the MB-FD and MB-BE ones. Moreover, our model allows us to treat the three-state mixing FD-MB-BE intermediate statistics. For boson and fermion mixing in a D-dimensional space, we obtain a family of FD-BE intermediate statistics by varying the transmutational potential η BF . This family contains, as a particular case when η BF =0, the quantum statistics recently proposed by L. Wu, Z. Wu, and J. Sun [Phys. Lett. A 170, 280 (1992)]. When we consider the two-dimensional FD-BE statistics, we derive an analytic expression of the fraction of fermions. When the temperature T→∞, the system is composed by an equal number of bosons and fermions, regardless of the value of η BF . On the contrary, when T=0, η BF becomes important and, according to its value, the system can be completely bosonic or fermionic, or composed both by bosons and fermions
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Medical School Attrition-Beyond the Statistics A Ten Year Retrospective Study
Directory of Open Access Journals (Sweden)
Maher Bridget M
2013-01-01
Full Text Available Abstract Background Medical school attrition is important - securing a place in medical school is difficult and a high attrition rate can affect the academic reputation of a medical school and staff morale. More important, however, are the personal consequences of dropout for the student. The aims of our study were to examine factors associated with attrition over a ten-year period (2001–2011 and to study the personal effects of dropout on individual students. Methods The study included quantitative analysis of completed cohorts and qualitative analysis of ten-year data. Data were collected from individual student files, examination and admission records, exit interviews and staff interviews. Statistical analysis was carried out on five successive completed cohorts. Qualitative data from student files was transcribed and independently analysed by three authors. Data was coded and categorized and key themes were identified. Results Overall attrition rate was 5.7% (45/779 in 6 completed cohorts when students who transferred to other medical courses were excluded. Students from Kuwait and United Arab Emirates had the highest dropout rate (RR = 5.70, 95% Confidence Intervals 2.65 to 12.27;p Absenteeism was documented in 30% of students, academic difficulty in 55.7%, social isolation in 20%, and psychological morbidity in 40% (higher than other studies. Qualitative analysis revealed recurrent themes of isolation, failure, and despair. Student Welfare services were only accessed by one-third of dropout students. Conclusions While dropout is often multifactorial, certain red flag signals may alert us to risk of dropout including non-EU origin, academic struggling, absenteeism, social isolation, depression and leave of absence. Psychological morbidity amongst dropout students is high and Student Welfare services should be actively promoted. Absenteeism should prompt early intervention. Behind every dropout statistic lies a personal story. All
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Probing NWP model deficiencies by statistical postprocessing
DEFF Research Database (Denmark)
Rosgaard, Martin Haubjerg; Nielsen, Henrik Aalborg; Nielsen, Torben S.
2016-01-01
The objective in this article is twofold. On one hand, a Model Output Statistics (MOS) framework for improved wind speed forecast accuracy is described and evaluated. On the other hand, the approach explored identifies unintuitive explanatory value from a diagnostic variable in an operational....... Based on the statistical model candidates inferred from the data, the lifted index NWP model diagnostic is consistently found among the NWP model predictors of the best performing statistical models across sites....
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Statistical Model of Extreme Shear
DEFF Research Database (Denmark)
Hansen, Kurt Schaldemose; Larsen, Gunner Chr.
2005-01-01
In order to continue cost-optimisation of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate...
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Aspects of statistical model for multifragmentation
International Nuclear Information System (INIS)
Bhattacharyya, P.; Das Gupta, S.; Mekjian, A. Z.
1999-01-01
We deal with two different aspects of an exactly soluble statistical model of fragmentation. First we show, using zero range force and finite temperature Thomas-Fermi theory, that a common link can be found between finite temperature mean field theory and the statistical fragmentation model. We show the latter naturally arises in the spinodal region. Next we show that although the exact statistical model is a canonical model and uses temperature, microcanonical results which use constant energy rather than constant temperature can also be obtained from the canonical model using saddle-point approximation. The methodology is extremely simple to implement and at least in all the examples studied in this work is very accurate. (c) 1999 The American Physical Society
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A basket two-part model to analyze medical expenditure on interdependent multiple sectors.
Sugawara, Shinya; Wu, Tianyi; Yamanishi, Kenji
2018-05-01
This study proposes a novel statistical methodology to analyze expenditure on multiple medical sectors using consumer data. Conventionally, medical expenditure has been analyzed by two-part models, which separately consider purchase decision and amount of expenditure. We extend the traditional two-part models by adding the step of basket analysis for dimension reduction. This new step enables us to analyze complicated interdependence between multiple sectors without an identification problem. As an empirical application for the proposed method, we analyze data of 13 medical sectors from the Medical Expenditure Panel Survey. In comparison with the results of previous studies that analyzed the multiple sector independently, our method provides more detailed implications of the impacts of individual socioeconomic status on the composition of joint purchases from multiple medical sectors; our method has a better prediction performance.
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Statistical Compression for Climate Model Output
Hammerling, D.; Guinness, J.; Soh, Y. J.
2017-12-01
Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus is it important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset-one year of daily mean temperature data-particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers.
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Automated statistical modeling of analytical measurement systems
International Nuclear Information System (INIS)
Jacobson, J.J.
1992-01-01
The statistical modeling of analytical measurement systems at the Idaho Chemical Processing Plant (ICPP) has been completely automated through computer software. The statistical modeling of analytical measurement systems is one part of a complete quality control program used by the Remote Analytical Laboratory (RAL) at the ICPP. The quality control program is an integration of automated data input, measurement system calibration, database management, and statistical process control. The quality control program and statistical modeling program meet the guidelines set forth by the American Society for Testing Materials and American National Standards Institute. A statistical model is a set of mathematical equations describing any systematic bias inherent in a measurement system and the precision of a measurement system. A statistical model is developed from data generated from the analysis of control standards. Control standards are samples which are made up at precise known levels by an independent laboratory and submitted to the RAL. The RAL analysts who process control standards do not know the values of those control standards. The object behind statistical modeling is to describe real process samples in terms of their bias and precision and, to verify that a measurement system is operating satisfactorily. The processing of control standards gives us this ability
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Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.
Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J
2016-10-03
Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.
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Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
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Statistical modelling for ship propulsion efficiency
DEFF Research Database (Denmark)
Petersen, Jóan Petur; Jacobsen, Daniel J.; Winther, Ole
2012-01-01
This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks...
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Statistical physics of medical diagnostics: Study of a probabilistic model.
Mashaghi, Alireza; Ramezanpour, Abolfazl
2018-03-01
We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.
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Statistical physics of medical diagnostics: Study of a probabilistic model
Mashaghi, Alireza; Ramezanpour, Abolfazl
2018-03-01
We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.
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Sensometrics: Thurstonian and Statistical Models
DEFF Research Database (Denmark)
Christensen, Rune Haubo Bojesen
. sensR is a package for sensory discrimination testing with Thurstonian models and ordinal supports analysis of ordinal data with cumulative link (mixed) models. While sensR is closely connected to the sensometrics field, the ordinal package has developed into a generic statistical package applicable......This thesis is concerned with the development and bridging of Thurstonian and statistical models for sensory discrimination testing as applied in the scientific discipline of sensometrics. In sensory discrimination testing sensory differences between products are detected and quantified by the use...... and sensory discrimination testing in particular in a series of papers by advancing Thurstonian models for a range of sensory discrimination protocols in addition to facilitating their application by providing software for fitting these models. The main focus is on identifying Thurstonian models...
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WE-A-201-00: Anne and Donald Herbert Distinguished Lectureship On Modern Statistical Modeling
Energy Technology Data Exchange (ETDEWEB)
NONE
2016-06-15
Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear
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WE-A-201-00: Anne and Donald Herbert Distinguished Lectureship On Modern Statistical Modeling
International Nuclear Information System (INIS)
2016-01-01
Chris Marshall: Memorial Introduction Donald Edmonds Herbert Jr., or Don to his colleagues and friends, exemplified the “big tent” vision of medical physics, specializing in Applied Statistics and Dynamical Systems theory. He saw, more clearly than most, that “Making models is the difference between doing science and just fooling around [ref Woodworth, 2004]”. Don developed an interest in chemistry at school by “reading a book” - a recurring theme in his story. He was awarded a Westinghouse Science scholarship and attended the Carnegie Institute of Technology (later Carnegie Mellon University) where his interest turned to physics and led to a BS in Physics after transfer to Northwestern University. After (voluntary) service in the Navy he earned his MS in Physics from the University of Oklahoma, which led him to Johns Hopkins University in Baltimore to pursue a PhD. The early death of his wife led him to take a salaried position in the Physics Department of Colorado College in Colorado Springs so as to better care for their young daughter. There, a chance invitation from Dr. Juan del Regato to teach physics to residents at the Penrose Cancer Hospital introduced him to Medical Physics, and he decided to enter the field. He received his PhD from the University of London (UK) under Prof. Joseph Rotblat, where I first met him, and where he taught himself statistics. He returned to Penrose as a clinical medical physicist, also largely self-taught. In 1975 he formalized an evolving interest in statistical analysis as Professor of Radiology and Head of the Division of Physics and Statistics at the College of Medicine of the University of South Alabama in Mobile, AL where he remained for the rest of his career. He also served as the first Director of their Bio-Statistics and Epidemiology Core Unit working in part on a sickle-cell disease. After retirement he remained active as Professor Emeritus. Don served for several years as a consultant to the Nuclear
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Statistical modelling for social researchers principles and practice
Tarling, Roger
2008-01-01
This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-l...
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A Baseline Patient Model to Support Testing of Medical Cyber-Physical Systems.
Silva, Lenardo C; Perkusich, Mirko; Almeida, Hyggo O; Perkusich, Angelo; Lima, Mateus A M; Gorgônio, Kyller C
2015-01-01
Medical Cyber-Physical Systems (MCPS) are currently a trending topic of research. The main challenges are related to the integration and interoperability of connected medical devices, patient safety, physiologic closed-loop control, and the verification and validation of these systems. In this paper, we focus on patient safety and MCPS validation. We present a formal patient model to be used in health care systems validation without jeopardizing the patient's health. To determine the basic patient conditions, our model considers the four main vital signs: heart rate, respiratory rate, blood pressure and body temperature. To generate the vital signs we used regression models based on statistical analysis of a clinical database. Our solution should be used as a starting point for a behavioral patient model and adapted to specific clinical scenarios. We present the modeling process of the baseline patient model and show its evaluation. The conception process may be used to build different patient models. The results show the feasibility of the proposed model as an alternative to the immediate need for clinical trials to test these medical systems.
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Topology for statistical modeling of petascale data.
Energy Technology Data Exchange (ETDEWEB)
Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)
2011-07-01
This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.
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Bayesian models: A statistical primer for ecologists
Hobbs, N. Thompson; Hooten, Mevin B.
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models
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Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R
2014-02-01
The possibility and likelihood of a postoperative medical complication after spine surgery undoubtedly play a major role in the decision making of the surgeon and patient alike. Although prior study has determined relative risk and odds ratio values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of medical complication, rather than relative risk or odds ratio values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of medical complication during and after spine surgery. Statistical analysis using a prospective surgical spine registry that recorded extensive demographic, surgical, and complication data. Outcomes examined are medical complications that were specifically defined a priori. This analysis is a continuation of statistical analysis of our previously published report. Using a prospectively collected surgical registry of more than 1,476 patients with extensive demographic, comorbidity, surgical, and complication detail recorded for 2 years after surgery, we previously identified several risk factor for medical complications. Using the beta coefficients from those log binomial regression analyses, we created a model to predict the occurrence of medical complication after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created two predictive models: one predicting the occurrence of any medical complication and the other predicting the occurrence of a major medical complication. The final predictive model for any medical complications had a receiver operator curve characteristic of 0.76, considered to be a fair measure. The final predictive model for any major medical complications had
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Statistical Model-Based Face Pose Estimation
Institute of Scientific and Technical Information of China (English)
GE Xinliang; YANG Jie; LI Feng; WANG Huahua
2007-01-01
A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.
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Attitude of teaching faculty towards statistics at a medical university in Karachi, Pakistan.
Khan, Nazeer; Mumtaz, Yasmin
2009-01-01
Statistics is mainly used in biological research to verify the clinicians and researchers findings and feelings, and gives scientific validity for their inferences. In Pakistan, the educational curriculum is developed in such a way that the students who are interested in entering in the field of biological sciences do not study mathematics after grade 10. Therefore, due to their fragile background of mathematical skills, the Pakistani medical professionals feel that they do not have adequate base to understand the basic concepts of statistical techniques when they try to use it in their research or read a scientific article. The aim of the study was to assess the attitude of medical faculty towards statistics. A questionnaire containing 42 close-ended and 4 open-ended questions, related to the attitude and knowledge of statistics, was distributed among the teaching faculty of Dow University of Health Sciences (DUHS). One hundred and sixty-seven filled questionnaires were returned from 374 faculty members (response rate 44.7%). Forty-three percent of the respondents claimed that they had 'introductive' level of statistics courses, 63% of the respondents strongly agreed that a good researcher must have some training in statistics, 82% of the faculty was in favour (strongly agreed or agreed) that statistics was really useful for research. Only 17% correctly stated that statistics is the science of uncertainty. Half of the respondents accepted that they have problem of writing the statistical section of the article. 64% of the subjects indicated that statistical teaching methods were the main reasons for the impression of its difficulties. 53% of the faculty indicated that the co-authorship of the statistician should depend upon his/her contribution in the study. Gender did not show any significant difference among the responses. However, senior faculty showed higher level of the importance for the use of statistics and difficulties of writing result section of
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International Nuclear Information System (INIS)
Bruse, Jan L.; McLeod, Kristin; Biglino, Giovanni; Ntsinjana, Hopewell N.; Capelli, Claudio
2016-01-01
Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient’s anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. The suggested method has the potential to discover previously
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Simple statistical model for branched aggregates
DEFF Research Database (Denmark)
Lemarchand, Claire; Hansen, Jesper Schmidt
2015-01-01
, given that it already has bonds with others. The model is applied here to asphaltene nanoaggregates observed in molecular dynamics simulations of Cooee bitumen. The variation with temperature of the probabilities deduced from this model is discussed in terms of statistical mechanics arguments....... The relevance of the statistical model in the case of asphaltene nanoaggregates is checked by comparing the predicted value of the probability for one molecule to have exactly i bonds with the same probability directly measured in the molecular dynamics simulations. The agreement is satisfactory......We propose a statistical model that can reproduce the size distribution of any branched aggregate, including amylopectin, dendrimers, molecular clusters of monoalcohols, and asphaltene nanoaggregates. It is based on the conditional probability for one molecule to form a new bond with a molecule...
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Matrix Tricks for Linear Statistical Models
Puntanen, Simo; Styan, George PH
2011-01-01
In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and
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Statistical Model Checking of Rich Models and Properties
DEFF Research Database (Denmark)
Poulsen, Danny Bøgsted
in undecidability issues for the traditional model checking approaches. Statistical model checking has proven itself a valuable supplement to model checking and this thesis is concerned with extending this software validation technique to stochastic hybrid systems. The thesis consists of two parts: the first part...... motivates why existing model checking technology should be supplemented by new techniques. It also contains a brief introduction to probability theory and concepts covered by the six papers making up the second part. The first two papers are concerned with developing online monitoring techniques...... systems. The fifth paper shows how stochastic hybrid automata are useful for modelling biological systems and the final paper is concerned with showing how statistical model checking is efficiently distributed. In parallel with developing the theory contained in the papers, a substantial part of this work...
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Statistical Modelling of Wind Proles - Data Analysis and Modelling
DEFF Research Database (Denmark)
Jónsson, Tryggvi; Pinson, Pierre
The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....
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Statistical physics of pairwise probability models
DEFF Research Database (Denmark)
Roudi, Yasser; Aurell, Erik; Hertz, John
2009-01-01
(dansk abstrakt findes ikke) Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data......: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying...
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Butler, D. J.; Kerstman, E.; Saile, L.; Myers, J.; Walton, M.; Lopez, V.; McGrath, T.
2011-01-01
The Integrated Medical Model (IMM) captures organizational knowledge across the space medicine, training, operations, engineering, and research domains. IMM uses this knowledge in the context of a mission and crew profile to forecast risks to crew health and mission success. The IMM establishes a quantified, statistical relationship among medical conditions, risk factors, available medical resources, and crew health and mission outcomes. These relationships may provide an appropriate foundation for developing an in-flight medical decision support tool that helps optimize the use of medical resources and assists in overall crew health management by an autonomous crew with extremely limited interactions with ground support personnel and no chance of resupply.
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Implementation of statistical analysis methods for medical physics data
International Nuclear Information System (INIS)
Teixeira, Marilia S.; Pinto, Nivia G.P.; Barroso, Regina C.; Oliveira, Luis F.
2009-01-01
The objective of biomedical research with different radiation natures is to contribute for the understanding of the basic physics and biochemistry of the biological systems, the disease diagnostic and the development of the therapeutic techniques. The main benefits are: the cure of tumors through the therapy, the anticipated detection of diseases through the diagnostic, the using as prophylactic mean for blood transfusion, etc. Therefore, for the better understanding of the biological interactions occurring after exposure to radiation, it is necessary for the optimization of therapeutic procedures and strategies for reduction of radioinduced effects. The group pf applied physics of the Physics Institute of UERJ have been working in the characterization of biological samples (human tissues, teeth, saliva, soil, plants, sediments, air, water, organic matrixes, ceramics, fossil material, among others) using X-rays diffraction and X-ray fluorescence. The application of these techniques for measurement, analysis and interpretation of the biological tissues characteristics are experimenting considerable interest in the Medical and Environmental Physics. All quantitative data analysis must be initiated with descriptive statistic calculation (means and standard deviations) in order to obtain a previous notion on what the analysis will reveal. It is well known que o high values of standard deviation found in experimental measurements of biologicals samples can be attributed to biological factors, due to the specific characteristics of each individual (age, gender, environment, alimentary habits, etc). This work has the main objective the development of a program for the use of specific statistic methods for the optimization of experimental data an analysis. The specialized programs for this analysis are proprietary, another objective of this work is the implementation of a code which is free and can be shared by the other research groups. As the program developed since the
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Statistical evaluation of diagnostic performance topics in ROC analysis
Zou, Kelly H; Bandos, Andriy I; Ohno-Machado, Lucila; Rockette, Howard E
2016-01-01
Statistical evaluation of diagnostic performance in general and Receiver Operating Characteristic (ROC) analysis in particular are important for assessing the performance of medical tests and statistical classifiers, as well as for evaluating predictive models or algorithms. This book presents innovative approaches in ROC analysis, which are relevant to a wide variety of applications, including medical imaging, cancer research, epidemiology, and bioinformatics. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis covers areas including monotone-transformation techniques in parametric ROC analysis, ROC methods for combined and pooled biomarkers, Bayesian hierarchical transformation models, sequential designs and inferences in the ROC setting, predictive modeling, multireader ROC analysis, and free-response ROC (FROC) methodology. The book is suitable for graduate-level students and researchers in statistics, biostatistics, epidemiology, public health, biomedical engineering, radiology, medi...
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Uncertainty the soul of modeling, probability & statistics
Briggs, William
2016-01-01
This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...
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Kay-Rivest, E; Varma, N; Scott, G M; Manoukian, J J; Desrosiers, M; Vaccani, J P; Nguyen, L H P
2017-02-27
The residency match is an important event in an aspiring physician's career. Otolaryngology - Head and Neck Surgery (OTL-HNS) is a surgical specialty that has enjoyed high numbers of applicants to its residency programs. However, recent trends in Canada show a decline in first-choice applicants to several surgical fields. Factors thought to influence a medical student's choice include role models, career opportunities and work-life balance. The notion of perceived competitiveness is a factor that has not yet been explored. This study sought to compare competitiveness of OTL-HNS, as perceived by Canadian medical students to residency match statistics published yearly by CaRMS (Canadian Residency Matching Service), with the hope of informing future decisions of surgical residency programs. An electronic survey was created and distributed to all medical students enrolled in the 17 Canadian medical schools. After gathering demographic information, students were asked to rank what they perceived to be the five most competitive disciplines offered by CaRMS. They were also asked to rank surgical specialties from most to least competitive. Publically available data from CaRMS was then collected and analyzed to determine actual competitiveness of admissions to Canadian OTL-HNS residency programs. 1194 students, from first to fourth year of medical school, completed the survey. CaRMS statistics over the period from 2008 to 2014 demonstrated that the five most competitive specialties were Plastic Surgery, Dermatology, Ophthalmology, Emergency Medicine and OTL-HNS. Among surgical disciplines, OTL-HNS was third most competitive, where on average 72% of students match to their first-choice discipline. When students were questioned, 35% ranked OTL-HNS amongst the top five most competitive. On the other hand 72%, 74% and 80% recognized Opthalmology, Dermatology and Plastic Surgery as being among the five most competitive, respectively. We found that fourth-year medical students
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Cirera, Lluís; Salmerón, Diego; Martínez, Consuelo; Bañón, Rafael María; Navarro, Carmen
2018-06-06
After the return of Spain to democracy and the regional assumption of government powers, actions were initiated to improve the mortality statistics of death causes. The objective of this work was to describe the evolution of the quality activities improvements into the statistics of death causes on Murcia's region during 1989 to 2011. Descriptive epidemiological study of all death documents processed by the Murcia mortality registry. Use of indicators related to the quality of the completion of death in medical and judicial notification; recovery of information on the causes and circumstances of death; and impact on the statistics of ill-defined, unspecific and less specific causes. During the study period, the medical notification without a temporary sequence on the death certificate (DC) has decreased from 46% initial to 21% final (p less than 0.001). Information retrieval from sources was successful in 93% of the cases in 2001 compared to 38%, at the beginning of the period (p less than 0.001). Regional rates of ill-defined and unspecific causes fell more than national ones, and they were in the last year with a differential of 10.3 (p less than 0.001) and 2.8 points (p=0.001), respectively. The medical death certification improved in form and suitability. Regulated recovery of the causes of death and circumstances corrected medical and judicial information. The Murcia's region presented lower rates in less specified causes and ill-defined entities than national averages.
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Statistical Models for Social Networks
Snijders, Tom A. B.; Cook, KS; Massey, DS
2011-01-01
Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For
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Energy Technology Data Exchange (ETDEWEB)
Barboza, Adriana Elisa, E-mail: adrianaebarboza@gmail.com, E-mail: elisa@bolsista.ird.gov.br [Instituto de Radioprotecao e Dosimetria, (IRD/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2014-07-01
This work has as main purpose statistically estimating occupational exposure in medical diagnostic radiology in cases of high doses recorded in 2011 at national level. For statistical survey of this study, doses of 372 IOE's diagnostic radiology in different Brazilian states were evaluated. Data were extracted from the work of monograph (Research Methodology Of High Doses In Medical Radiodiagnostic) that contains the database's information Sector Management doses of IRD/CNEN-RJ, Brazil. The identification of these states allows the Sanitary Surveillance (VISA) responsible, becomes aware of events and work with programs to reduce these events. (author)
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Functional summary statistics for the Johnson-Mehl model
DEFF Research Database (Denmark)
Møller, Jesper; Ghorbani, Mohammad
The Johnson-Mehl germination-growth model is a spatio-temporal point process model which among other things have been used for the description of neurotransmitters datasets. However, for such datasets parametric Johnson-Mehl models fitted by maximum likelihood have yet not been evaluated by means...... of functional summary statistics. This paper therefore invents four functional summary statistics adapted to the Johnson-Mehl model, with two of them based on the second-order properties and the other two on the nuclei-boundary distances for the associated Johnson-Mehl tessellation. The functional summary...... statistics theoretical properties are investigated, non-parametric estimators are suggested, and their usefulness for model checking is examined in a simulation study. The functional summary statistics are also used for checking fitted parametric Johnson-Mehl models for a neurotransmitters dataset....
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Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P
1999-01-01
Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149
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Small nodule detectability evaluation using a generalized scan-statistic model
International Nuclear Information System (INIS)
Popescu, Lucretiu M; Lewitt, Robert M
2006-01-01
In this paper is investigated the use of the scan statistic for evaluating the detectability of small nodules in medical images. The scan-statistic method is often used in applications in which random fields must be searched for abnormal local features. Several results of the detection with localization theory are reviewed and a generalization is presented using the noise nodule distribution obtained by scanning arbitrary areas. One benefit of the noise nodule model is that it enables determination of the scan-statistic distribution by using only a few image samples in a way suitable both for simulation and experimental setups. Also, based on the noise nodule model, the case of multiple targets per image is addressed and an image abnormality test using the likelihood ratio and an alternative test using multiple decision thresholds are derived. The results obtained reveal that in the case of low contrast nodules or multiple nodules the usual test strategy based on a single decision threshold underperforms compared with the alternative tests. That is a consequence of the fact that not only the contrast or the size, but also the number of suspicious nodules is a clue indicating the image abnormality. In the case of the likelihood ratio test, the multiple clues are unified in a single decision variable. Other tests that process multiple clues differently do not necessarily produce a unique ROC curve, as shown in examples using a test involving two decision thresholds. We present examples with two-dimensional time-of-flight (TOF) and non-TOF PET image sets analysed using the scan statistic for different search areas, as well as the fixed position observer
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Distributions with given marginals and statistical modelling
Fortiana, Josep; Rodriguez-Lallena, José
2002-01-01
This book contains a selection of the papers presented at the meeting `Distributions with given marginals and statistical modelling', held in Barcelona (Spain), July 17-20, 2000. In 24 chapters, this book covers topics such as the theory of copulas and quasi-copulas, the theory and compatibility of distributions, models for survival distributions and other well-known distributions, time series, categorical models, definition and estimation of measures of dependence, monotonicity and stochastic ordering, shape and separability of distributions, hidden truncation models, diagonal families, orthogonal expansions, tests of independence, and goodness of fit assessment. These topics share the use and properties of distributions with given marginals, this being the fourth specialised text on this theme. The innovative aspect of the book is the inclusion of statistical aspects such as modelling, Bayesian statistics, estimation, and tests.
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Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment
Jaworske, D. A.; Myers, J. G.; Goodenow, D.; Young, M.; Arellano, J. D.
2016-01-01
Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.
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Actuarial statistics with generalized linear mixed models
Antonio, K.; Beirlant, J.
2007-01-01
Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics
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Quantitative Validation of the Integrated Medical Model (IMM) for ISS Missions
Young, Millennia; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Goodenow, D. A.; Myers, J. G.
2016-01-01
Lifetime Surveillance of Astronaut Health (LSAH) provided observed medical event data on 33 ISS and 111 STS person-missions for use in further improving and validating the Integrated Medical Model (IMM). Using only the crew characteristics from these observed missions, the newest development version, IMM v4.0, will simulate these missions to predict medical events and outcomes. Comparing IMM predictions to the actual observed medical event counts will provide external validation and identify areas of possible improvement. In an effort to improve the power of detecting differences in this validation study, the total over each program ISS and STS will serve as the main quantitative comparison objective, specifically the following parameters: total medical events (TME), probability of loss of crew life (LOCL), and probability of evacuation (EVAC). Scatter plots of observed versus median predicted TMEs (with error bars reflecting the simulation intervals) will graphically display comparisons while linear regression will serve as the statistical test of agreement. Two scatter plots will be analyzed 1) where each point reflects a mission and 2) where each point reflects a condition-specific total number of occurrences. The coefficient of determination (R2) resulting from a linear regression with no intercept bias (intercept fixed at zero) will serve as an overall metric of agreement between IMM and the real world system (RWS). In an effort to identify as many possible discrepancies as possible for further inspection, the -level for all statistical tests comparing IMM predictions to observed data will be set to 0.1. This less stringent criterion, along with the multiple testing being conducted, should detect all perceived differences including many false positive signals resulting from random variation. The results of these analyses will reveal areas of the model requiring adjustment to improve overall IMM output, which will thereby provide better decision support for
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Structured statistical models of inductive reasoning.
Kemp, Charles; Tenenbaum, Joshua B
2009-01-01
Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.
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Petrovecki, Mladen; Rahelić, Dario; Bilić-Zulle, Lidija; Jelec, Vjekoslav
2003-02-01
To investigate whether and to what extent various parameters, such as individual characteristics, computer habits, situational factors, and pseudoscientific variables, influence Medical Informatics examination grade, and how inadequate statistical analysis can lead to wrong conclusions. The study included a total of 382 second-year undergraduate students at the Rijeka University School of Medicine in the period from 1996/97 to 2000/01 academic year. After passing the Medical Informatics exam, students filled out an anonymous questionnaire about their attitude toward learning medical informatics. They were asked to grade the course organization and curriculum content, and provide their date of birth; sex; study year; high school grades; Medical Informatics examination grade, type, and term; and describe their computer habits. From these data, we determined their zodiac signs and biorhythm. Data were compared by the use of t-test, one-way ANOVA with Tukey's honest significance difference test, and randomized complete block design ANOVA. Out of 21 variables analyzed, only 10 correlated with the average grade. Students taking Medical Informatics examination in the 1998/99 academic year earned lower average grade than any other generation. Significantly higher Medical Informatics exam grade was earned by students who finished a grammar high school; owned and regularly used a computer, Internet, and e-mail (pzodiac sign, zodiac sign quality, or biorhythm cycles, except when intentionally inadequate statistics was used for data analysis. Medical Informatics examination grades correlated with general learning capacity and computer habits of students, but showed no relation to other investigated parameters, such as examination term or pseudoscientific parameters. Inadequate statistical analysis can always confirm false conclusions.
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Statistical modelling in biostatistics and bioinformatics selected papers
Peng, Defen
2014-01-01
This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...
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Sandhu, Amanjot; Fliker, Aviva; Leitao, Darren; Jones, Jodi; Gooi, Adrian
2017-01-01
Live-streaming video has had increasing uses in medical education, especially in distributed education models. The literature on the impact of live-streaming in non-distributed education models, however, is scarce. To determine the attitudes towards live-streaming and recorded lectures as a resource to pre-clerkship medical students in a non-distributed medical education model. First and second year medical students were sent a voluntary cross-sectional survey by email, and were asked questions on live-streaming, recorded lectures and in person lectures using a 5-point Likert and open answers. Of the 118 responses (54% response rate), the data suggested that both watching recorded lectures (Likert 4.55) and live-streaming lectures (4.09) were perceived to be more educationally valuable than face-to-face attendance of lectures (3.60). While responses indicated a statistically significant increase in anticipated classroom attendance if both live-streaming and recorded lectures were removed (from 63% attendance to 76%, p =0.002), there was no significant difference in attendance if live-streaming lectures were removed but recorded lectures were maintained (from 63% to 66%, p=0.76). The addition of live-streaming lectures in the pre-clerkship setting was perceived to be value added to the students. The data also suggests that the removal of live-streaming lectures would not lead to a statistically significant increase in classroom attendance by pre-clerkship students.
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The Ambulatory Integration of the Medical and Social (AIMS) model: A retrospective evaluation.
Rowe, Jeannine M; Rizzo, Victoria M; Shier Kricke, Gayle; Krajci, Kate; Rodriguez-Morales, Grisel; Newman, Michelle; Golden, Robyn
2016-01-01
An exploratory, retrospective evaluation of Ambulatory Integration of the Medical and Social (AIMS), a care coordination model designed to integrate medical and non-medical needs of patients and delivered exclusively by social workers was conducted to examine mean utilization of costly health care services for older adult patients. Results reveal mean utilization of 30-day hospital readmissions, emergency department (ED) visits, and hospital admissions are significantly lower for the study sample compared to the larger patient population. Comparisons with national population statistics reveal significantly lower mean utilization of 30-day admissions and ED visits for the study sample. The findings offer preliminary support regarding the value of AIMS.
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A Stochastic Fractional Dynamics Model of Rainfall Statistics
Kundu, Prasun; Travis, James
2013-04-01
Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, that allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is designed to faithfully reflect the scale dependence and is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and times scales. The main restriction is the assumption that the statistics of the precipitation field is spatially homogeneous and isotropic and stationary in time. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and in Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to the second moment statistics of the radar data. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well without any further adjustment. Some data sets containing periods of non-stationary behavior that involves occasional anomalously correlated rain events, present a challenge for the model.
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Statistical Models and Methods for Lifetime Data
Lawless, Jerald F
2011-01-01
Praise for the First Edition"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."-Choice"This is an important book, which will appeal to statisticians working on survival analysis problems."-Biometrics"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."-Statistics in MedicineThe statistical analysis of lifetime or response time data is a key tool in engineering,
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Integrated Medical Model – Chest Injury Model
National Aeronautics and Space Administration — The Exploration Medical Capability (ExMC) Element of NASA's Human Research Program (HRP) developed the Integrated Medical Model (IMM) to forecast the resources...
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Topology for Statistical Modeling of Petascale Data
Energy Technology Data Exchange (ETDEWEB)
Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Bremer, P. -T. [Univ. of Utah, Salt Lake City, UT (United States)
2013-10-31
Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.
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Statistical models and methods for reliability and survival analysis
Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo
2013-01-01
Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical
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A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model
Energy Technology Data Exchange (ETDEWEB)
Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-05-11
This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.
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Statistics corner: A guide to appropriate use of correlation coefficient in medical research.
Mukaka, M M
2012-09-01
Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all. The aim of this article is to provide a guide to appropriate use of correlation in medical research and to highlight some misuse. Examples of the applications of the correlation coefficient have been provided using data from statistical simulations as well as real data. Rule of thumb for interpreting size of a correlation coefficient has been provided.
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Model-generated air quality statistics for application in vegetation response models in Alberta
International Nuclear Information System (INIS)
McVehil, G.E.; Nosal, M.
1990-01-01
To test and apply vegetation response models in Alberta, air pollution statistics representative of various parts of the Province are required. At this time, air quality monitoring data of the requisite accuracy and time resolution are not available for most parts of Alberta. Therefore, there exists a need to develop appropriate air quality statistics. The objectives of the work reported here were to determine the applicability of model generated air quality statistics and to develop by modelling, realistic and representative time series of hourly SO 2 concentrations that could be used to generate the statistics demanded by vegetation response models
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Performance modeling, loss networks, and statistical multiplexing
Mazumdar, Ravi
2009-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I
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Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.
2017-12-01
Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.
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Statistical Models of Adaptive Immune populations
Sethna, Zachary; Callan, Curtis; Walczak, Aleksandra; Mora, Thierry
The availability of large (104-106 sequences) datasets of B or T cell populations from a single individual allows reliable fitting of complex statistical models for naïve generation, somatic selection, and hypermutation. It is crucial to utilize a probabilistic/informational approach when modeling these populations. The inferred probability distributions allow for population characterization, calculation of probability distributions of various hidden variables (e.g. number of insertions), as well as statistical properties of the distribution itself (e.g. entropy). In particular, the differences between the T cell populations of embryonic and mature mice will be examined as a case study. Comparing these populations, as well as proposed mixed populations, provides a concrete exercise in model creation, comparison, choice, and validation.
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Tropical geometry of statistical models.
Pachter, Lior; Sturmfels, Bernd
2004-11-16
This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.
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12th Workshop on Stochastic Models, Statistics and Their Applications
Rafajłowicz, Ewaryst; Szajowski, Krzysztof
2015-01-01
This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.
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Statistical Validation of Engineering and Scientific Models: Background
International Nuclear Information System (INIS)
Hills, Richard G.; Trucano, Timothy G.
1999-01-01
A tutorial is presented discussing the basic issues associated with propagation of uncertainty analysis and statistical validation of engineering and scientific models. The propagation of uncertainty tutorial illustrates the use of the sensitivity method and the Monte Carlo method to evaluate the uncertainty in predictions for linear and nonlinear models. Four example applications are presented; a linear model, a model for the behavior of a damped spring-mass system, a transient thermal conduction model, and a nonlinear transient convective-diffusive model based on Burger's equation. Correlated and uncorrelated model input parameters are considered. The model validation tutorial builds on the material presented in the propagation of uncertainty tutoriaI and uses the damp spring-mass system as the example application. The validation tutorial illustrates several concepts associated with the application of statistical inference to test model predictions against experimental observations. Several validation methods are presented including error band based, multivariate, sum of squares of residuals, and optimization methods. After completion of the tutorial, a survey of statistical model validation literature is presented and recommendations for future work are made
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Statistical image processing and multidimensional modeling
Fieguth, Paul
2010-01-01
Images are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a statistical image processing task: the inference of something - an artery, a road, a DNA marker, an oil spill - from imagery, possibly noisy, blurry, or incomplete. A great many textbooks have been written on image processing. However this book does not so much focus on images, per se, but rather on spatial data sets, with one or more measurements taken over
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Multiple commodities in statistical microeconomics: Model and market
Baaquie, Belal E.; Yu, Miao; Du, Xin
2016-11-01
A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.
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Statistical models for optimizing mineral exploration
International Nuclear Information System (INIS)
Wignall, T.K.; DeGeoffroy, J.
1987-01-01
The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)
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Statistical physics of pairwise probability models
Directory of Open Access Journals (Sweden)
Yasser Roudi
2009-11-01
Full Text Available Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.
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High-dimensional data: p >> n in mathematical statistics and bio-medical applications
Van De Geer, Sara A.; Van Houwelingen, Hans C.
2004-01-01
The workshop 'High-dimensional data: p >> n in mathematical statistics and bio-medical applications' was held at the Lorentz Center in Leiden from 9 to 20 September 2002. This special issue of Bernoulli contains a selection of papers presented at that workshop. ¶ The introduction of high-throughput micro-array technology to measure gene-expression levels and the publication of the pioneering paper by Golub et al. (1999) has brought to life a whole new branch of data analysis under the name of...
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Overcoming barriers to development of cooperative medical decision support models.
Hudson, Donna L; Cohen, Maurice E
2012-01-01
Attempts to automate the medical decision making process have been underway for the at least fifty years, beginning with data-based approaches that relied chiefly on statistically-based methods. Approaches expanded to include knowledge-based systems, both linear and non-linear neural networks, agent-based systems, and hybrid methods. While some of these models produced excellent results none have been used extensively in medical practice. In order to move these methods forward into practical use, a number of obstacles must be overcome, including validation of existing systems on large data sets, development of methods for including new knowledge as it becomes available, construction of a broad range of decision models, and development of non-intrusive methods that allow the physician to use these decision aids in conjunction with, not instead of, his or her own medical knowledge. None of these four requirements will come easily. A cooperative effort among researchers, including practicing MDs, is vital, particularly as more information on diseases and their contributing factors continues to expand resulting in more parameters than the human decision maker can process effectively. In this article some of the basic structures that are necessary to facilitate the use of an automated decision support system are discussed, along with potential methods for overcoming existing barriers.
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Statistical transmutation in doped quantum dimer models.
Lamas, C A; Ralko, A; Cabra, D C; Poilblanc, D; Pujol, P
2012-07-06
We prove a "statistical transmutation" symmetry of doped quantum dimer models on the square, triangular, and kagome lattices: the energy spectrum is invariant under a simultaneous change of statistics (i.e., bosonic into fermionic or vice versa) of the holes and of the signs of all the dimer resonance loops. This exact transformation enables us to define the duality equivalence between doped quantum dimer Hamiltonians and provides the analytic framework to analyze dynamical statistical transmutations. We investigate numerically the doping of the triangular quantum dimer model with special focus on the topological Z(2) dimer liquid. Doping leads to four (instead of two for the square lattice) inequivalent families of Hamiltonians. Competition between phase separation, superfluidity, supersolidity, and fermionic phases is investigated in the four families.
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Mesh Processing in Medical Image Analysis
DEFF Research Database (Denmark)
The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation....
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Textual information access statistical models
Gaussier, Eric
2013-01-01
This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access:- information extraction and retrieval;- text classification and clustering;- opinion mining;- comprehension aids (automatic summarization, machine translation, visualization).In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications
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Model for neural signaling leap statistics
International Nuclear Information System (INIS)
Chevrollier, Martine; Oria, Marcos
2011-01-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.
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Model for neural signaling leap statistics
Chevrollier, Martine; Oriá, Marcos
2011-03-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.
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Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
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Analytical modeling of worldwide medical radiation use
International Nuclear Information System (INIS)
Mettler, F.A. Jr.; Davis, M.; Kelsey, C.A.; Rosenberg, R.; Williams, A.
1987-01-01
An analytical model was developed to estimate the availability and frequency of medical radiation use on a worldwide basis. This model includes medical and dental x-ray, nuclear medicine, and radiation therapy. The development of an analytical model is necessary as the first step in estimating the radiation dose to the world's population from this source. Since there is no data about the frequency of medical radiation use in more than half the countries in the world and only fragmentary data in an additional one-fourth of the world's countries, such a model can be used to predict the uses of medical radiation in these countries. The model indicates that there are approximately 400,000 medical x-ray machines worldwide and that approximately 1.2 billion diagnostic medical x-ray examinations are performed annually. Dental x-ray examinations are estimated at 315 million annually and approximately 22 million in-vivo diagnostic nuclear medicine examinations. Approximately 4 million radiation therapy procedures or courses of treatment are undertaken annually
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The health of the American slave examined by means of Union Army medical statistics.
Freemon, F R
1985-01-01
The health status of the American slave in the 19th century remains unclear despite extensive historical research. Better knowledge of slave health would provide a clearer picture of the life of the slave, a better understanding of the 19th-century medicine, and possibly even clues to the health problems of modern blacks. This article hopes to contribute to the literature by examining another source of data. Slaves entering the Union Army joined an organization with standardized medical care that generated extensive statistical information. Review of these statistics answers questions about the health of young male blacks at the time American slavery ended.
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Equilibrium statistical mechanics of lattice models
Lavis, David A
2015-01-01
Most interesting and difficult problems in equilibrium statistical mechanics concern models which exhibit phase transitions. For graduate students and more experienced researchers this book provides an invaluable reference source of approximate and exact solutions for a comprehensive range of such models. Part I contains background material on classical thermodynamics and statistical mechanics, together with a classification and survey of lattice models. The geometry of phase transitions is described and scaling theory is used to introduce critical exponents and scaling laws. An introduction is given to finite-size scaling, conformal invariance and Schramm—Loewner evolution. Part II contains accounts of classical mean-field methods. The parallels between Landau expansions and catastrophe theory are discussed and Ginzburg—Landau theory is introduced. The extension of mean-field theory to higher-orders is explored using the Kikuchi—Hijmans—De Boer hierarchy of approximations. In Part III the use of alge...
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Computational and Statistical Models: A Comparison for Policy Modeling of Childhood Obesity
Mabry, Patricia L.; Hammond, Ross; Ip, Edward Hak-Sing; Huang, Terry T.-K.
As systems science methodologies have begun to emerge as a set of innovative approaches to address complex problems in behavioral, social science, and public health research, some apparent conflicts with traditional statistical methodologies for public health have arisen. Computational modeling is an approach set in context that integrates diverse sources of data to test the plausibility of working hypotheses and to elicit novel ones. Statistical models are reductionist approaches geared towards proving the null hypothesis. While these two approaches may seem contrary to each other, we propose that they are in fact complementary and can be used jointly to advance solutions to complex problems. Outputs from statistical models can be fed into computational models, and outputs from computational models can lead to further empirical data collection and statistical models. Together, this presents an iterative process that refines the models and contributes to a greater understanding of the problem and its potential solutions. The purpose of this panel is to foster communication and understanding between statistical and computational modelers. Our goal is to shed light on the differences between the approaches and convey what kinds of research inquiries each one is best for addressing and how they can serve complementary (and synergistic) roles in the research process, to mutual benefit. For each approach the panel will cover the relevant "assumptions" and how the differences in what is assumed can foster misunderstandings. The interpretations of the results from each approach will be compared and contrasted and the limitations for each approach will be delineated. We will use illustrative examples from CompMod, the Comparative Modeling Network for Childhood Obesity Policy. The panel will also incorporate interactive discussions with the audience on the issues raised here.
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Spherical Process Models for Global Spatial Statistics
Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.
2017-01-01
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture
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Model for neural signaling leap statistics
Energy Technology Data Exchange (ETDEWEB)
Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)
2011-03-01
We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.
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Analysis and Evaluation of Statistical Models for Integrated Circuits Design
Directory of Open Access Journals (Sweden)
Sáenz-Noval J.J.
2011-10-01
Full Text Available Statistical models for integrated circuits (IC allow us to estimate the percentage of acceptable devices in the batch before fabrication. Actually, Pelgrom is the statistical model most accepted in the industry; however it was derived from a micrometer technology, which does not guarantee reliability in nanometric manufacturing processes. This work considers three of the most relevant statistical models in the industry and evaluates their limitations and advantages in analog design, so that the designer has a better criterion to make a choice. Moreover, it shows how several statistical models can be used for each one of the stages and design purposes.
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The issue of statistical power for overall model fit in evaluating structural equation models
Directory of Open Access Journals (Sweden)
Richard HERMIDA
2015-06-01
Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.
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Understanding and forecasting polar stratospheric variability with statistical models
Directory of Open Access Journals (Sweden)
C. Blume
2012-07-01
Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.
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Improved model for statistical alignment
Energy Technology Data Exchange (ETDEWEB)
Miklos, I.; Toroczkai, Z. (Zoltan)
2001-01-01
The statistical approach to molecular sequence evolution involves the stochastic modeling of the substitution, insertion and deletion processes. Substitution has been modeled in a reliable way for more than three decades by using finite Markov-processes. Insertion and deletion, however, seem to be more difficult to model, and thc recent approaches cannot acceptably deal with multiple insertions and deletions. A new method based on a generating function approach is introduced to describe the multiple insertion process. The presented algorithm computes the approximate joint probability of two sequences in 0(13) running time where 1 is the geometric mean of the sequence lengths.
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Directory of Open Access Journals (Sweden)
Rafdzah Zaki
2013-06-01
Full Text Available Objective(s: Reliability measures precision or the extent to which test results can be replicated. This is the first ever systematic review to identify statistical methods used to measure reliability of equipment measuring continuous variables. This studyalso aims to highlight the inappropriate statistical method used in the reliability analysis and its implication in the medical practice. Materials and Methods: In 2010, five electronic databases were searched between 2007 and 2009 to look for reliability studies. A total of 5,795 titles were initially identified. Only 282 titles were potentially related, and finally 42 fitted the inclusion criteria. Results: The Intra-class Correlation Coefficient (ICC is the most popular method with 25 (60% studies having used this method followed by the comparing means (8 or 19%. Out of 25 studies using the ICC, only 7 (28% reported the confidence intervals and types of ICC used. Most studies (71% also tested the agreement of instruments. Conclusion: This study finds that the Intra-class Correlation Coefficient is the most popular method used to assess the reliability of medical instruments measuring continuous outcomes. There are also inappropriate applications and interpretations of statistical methods in some studies. It is important for medical researchers to be aware of this issue, and be able to correctly perform analysis in reliability studies.
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Daily precipitation statistics in regional climate models
DEFF Research Database (Denmark)
Frei, Christoph; Christensen, Jens Hesselbjerg; Déqué, Michel
2003-01-01
An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable-resolution global model are considered, all with a grid spacing of 50 km...
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Infinite Random Graphs as Statistical Mechanical Models
DEFF Research Database (Denmark)
Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria
2011-01-01
We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe a ...
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A statistical model for predicting muscle performance
Byerly, Diane Leslie De Caix
The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing
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An R2 statistic for fixed effects in the linear mixed model.
Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver
2008-12-20
Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.
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Mixed deterministic statistical modelling of regional ozone air pollution
Kalenderski, Stoitchko
2011-03-17
We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..
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Adaptive Maneuvering Frequency Method of Current Statistical Model
Institute of Scientific and Technical Information of China (English)
Wei Sun; Yongjian Yang
2017-01-01
Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.
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Speech emotion recognition based on statistical pitch model
Institute of Scientific and Technical Information of China (English)
WANG Zhiping; ZHAO Li; ZOU Cairong
2006-01-01
A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.
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Statistical modelling of citation exchange between statistics journals.
Varin, Cristiano; Cattelan, Manuela; Firth, David
2016-01-01
Rankings of scholarly journals based on citation data are often met with scepticism by the scientific community. Part of the scepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of researchers. The paper focuses on analysis of the table of cross-citations among a selection of statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care to avoid potential overinterpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's research assessment exercise shows strong correlation at aggregate level between assessed research quality and journal citation 'export scores' within the discipline of statistics.
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Statistical validation of normal tissue complication probability models.
Xu, Cheng-Jian; van der Schaaf, Arjen; Van't Veld, Aart A; Langendijk, Johannes A; Schilstra, Cornelis
2012-09-01
To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use. Copyright © 2012 Elsevier Inc. All rights reserved.
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Statistical Validation of Normal Tissue Complication Probability Models
Energy Technology Data Exchange (ETDEWEB)
Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schilstra, Cornelis [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Radiotherapy Institute Friesland, Leeuwarden (Netherlands)
2012-09-01
Purpose: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. Methods and Materials: A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Results: Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Conclusion: Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.
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Shell model in large spaces and statistical spectroscopy
International Nuclear Information System (INIS)
Kota, V.K.B.
1996-01-01
For many nuclear structure problems of current interest it is essential to deal with shell model in large spaces. For this, three different approaches are now in use and two of them are: (i) the conventional shell model diagonalization approach but taking into account new advances in computer technology; (ii) the shell model Monte Carlo method. A brief overview of these two methods is given. Large space shell model studies raise fundamental questions regarding the information content of the shell model spectrum of complex nuclei. This led to the third approach- the statistical spectroscopy methods. The principles of statistical spectroscopy have their basis in nuclear quantum chaos and they are described (which are substantiated by large scale shell model calculations) in some detail. (author)
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Advances in statistical models for data analysis
Minerva, Tommaso; Vichi, Maurizio
2015-01-01
This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.
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Computationally efficient statistical differential equation modeling using homogenization
Hooten, Mevin B.; Garlick, Martha J.; Powell, James A.
2013-01-01
Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.
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Charan, J; Saxena, D
2014-01-01
Biased negative studies not only reflect poor research effort but also have an impact on 'patient care' as they prevent further research with similar objectives, leading to potential research areas remaining unexplored. Hence, published 'negative studies' should be methodologically strong. All parameters that may help a reader to judge validity of results and conclusions should be reported in published negative studies. There is a paucity of data on reporting of statistical and methodological parameters in negative studies published in Indian Medical Journals. The present systematic review was designed with an aim to critically evaluate negative studies published in prominent Indian Medical Journals for reporting of statistical and methodological parameters. Systematic review. All negative studies published in 15 Science Citation Indexed (SCI) medical journals published from India were included in present study. Investigators involved in the study evaluated all negative studies for the reporting of various parameters. Primary endpoints were reporting of "power" and "confidence interval." Power was reported in 11.8% studies. Confidence interval was reported in 15.7% studies. Majority of parameters like sample size calculation (13.2%), type of sampling method (50.8%), name of statistical tests (49.1%), adjustment of multiple endpoints (1%), post hoc power calculation (2.1%) were reported poorly. Frequency of reporting was more in clinical trials as compared to other study designs and in journals having impact factor more than 1 as compared to journals having impact factor less than 1. Negative studies published in prominent Indian medical journals do not report statistical and methodological parameters adequately and this may create problems in the critical appraisal of findings reported in these journals by its readers.
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Models for probability and statistical inference theory and applications
Stapleton, James H
2007-01-01
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...
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Fluctuations and correlations in statistical models of hadron production
International Nuclear Information System (INIS)
Gorenstein, M. I.
2012-01-01
An extension of the standard concept of the statistical ensembles is suggested. Namely, the statistical ensembles with extensive quantities fluctuating according to an externally given distribution are introduced. Applications in the statistical models of multiple hadron production in high energy physics are discussed.
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Defending the Counseling Model and Its Eventual Synthesis with the Medical Model.
Farrell, Madison, III
This paper explores the strengths and weaknesses of the medical model in an effort to promote a counseling perspective that embraces some of the medical models strengths. Through inclusion, rather than exclusion, it is believed that the counseling model will eventually infiltrate the medical model by way of its own documentation system. With its…
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Advanced data analysis in neuroscience integrating statistical and computational models
Durstewitz, Daniel
2017-01-01
This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerfu...
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Borsboom, D.; Haig, B.D.
2013-01-01
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science
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Cellular automata and statistical mechanical models
International Nuclear Information System (INIS)
Rujan, P.
1987-01-01
The authors elaborate on the analogy between the transfer matrix of usual lattice models and the master equation describing the time development of cellular automata. Transient and stationary properties of probabilistic automata are linked to surface and bulk properties, respectively, of restricted statistical mechanical systems. It is demonstrated that methods of statistical physics can be successfully used to describe the dynamic and the stationary behavior of such automata. Some exact results are derived, including duality transformations, exact mappings, disorder, and linear solutions. Many examples are worked out in detail to demonstrate how to use statistical physics in order to construct cellular automata with desired properties. This approach is considered to be a first step toward the design of fully parallel, probabilistic systems whose computational abilities rely on the cooperative behavior of their components
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Growth Curve Models and Applications : Indian Statistical Institute
2017-01-01
Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of app lied work, and these contributions have been externally r...
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Improving statistical reasoning theoretical models and practical implications
Sedlmeier, Peter
1999-01-01
This book focuses on how statistical reasoning works and on training programs that can exploit people''s natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.
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Directory of Open Access Journals (Sweden)
O. E. Karpov
2018-01-01
performance variables.Based on these definitions, the article describes the system for developing multidimensional analytical reporting, examines the stages of designing a multidimensional analytical representation of data, and demonstrates how to set up a multidimensional analytical report for building a multi-level hierarchy in accordance with pre-selected performance variables and dimensions.Methods of working with multidimensional analytical reporting are also described on the example of the implementation of the formation of the multidimensional analytical report «Statistical reporting on salaries» in the information system of the Federal State Budgetary Institution «NationalPirogovMedicalSurgicalCenter» of Ministry of Health of theRussian Federation.As a result, the effectiveness of multidimensional analytical reports for collecting, monitoring and analyzing statistical information on salaries was proved. Conducting in-depth analysis and evaluating the results of the implementation of salary targets are the basis for further modeling of the pay system and forecasting of financial results. Thus, the introduction of multidimensional analytical reporting made it possible to facilitate the labor-intensive process of making strategic management decisions by the administration of the institution.The authors concluded that the availability of such a tool in the healthcare system could help accelerating the operational processing of information for data analysis, as well as the generation of reports in various sections with an established depth of detail.
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Solar radiation data - statistical analysis and simulation models
Energy Technology Data Exchange (ETDEWEB)
Mustacchi, C; Cena, V; Rocchi, M; Haghigat, F
1984-01-01
The activities consisted in collecting meteorological data on magnetic tape for ten european locations (with latitudes ranging from 42/sup 0/ to 56/sup 0/ N), analysing the multi-year sequences, developing mathematical models to generate synthetic sequences having the same statistical properties of the original data sets, and producing one or more Short Reference Years (SRY's) for each location. The meteorological parameters examinated were (for all the locations) global + diffuse radiation on horizontal surface, dry bulb temperature, sunshine duration. For some of the locations additional parameters were available, namely, global, beam and diffuse radiation on surfaces other than horizontal, wet bulb temperature, wind velocity, cloud type, cloud cover. The statistical properties investigated were mean, variance, autocorrelation, crosscorrelation with selected parameters, probability density function. For all the meteorological parameters, various mathematical models were built: linear regression, stochastic models of the AR and the DAR type. In each case, the model with the best statistical behaviour was selected for the production of a SRY for the relevant parameter/location.
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Integrated Medical Model Overview
Myers, J.; Boley, L.; Foy, M.; Goodenow, D.; Griffin, D.; Keenan, A.; Kerstman, E.; Melton, S.; McGuire, K.; Saile, L.;
2015-01-01
The Integrated Medical Model (IMM) Project represents one aspect of NASA's Human Research Program (HRP) to quantitatively assess medical risks to astronauts for existing operational missions as well as missions associated with future exploration and commercial space flight ventures. The IMM takes a probabilistic approach to assessing the likelihood and specific outcomes of one hundred medical conditions within the envelope of accepted space flight standards of care over a selectable range of mission capabilities. A specially developed Integrated Medical Evidence Database (iMED) maintains evidence-based, organizational knowledge across a variety of data sources. Since becoming operational in 2011, version 3.0 of the IMM, the supporting iMED, and the expertise of the IMM project team have contributed to a wide range of decision and informational processes for the space medical and human research community. This presentation provides an overview of the IMM conceptual architecture and range of application through examples of actual space flight community questions posed to the IMM project.
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Statistical Model Checking for Biological Systems
DEFF Research Database (Denmark)
David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel
2014-01-01
Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic...
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Right-sizing statistical models for longitudinal data.
Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M
2015-12-01
Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).
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Statistical models based on conditional probability distributions
International Nuclear Information System (INIS)
Narayanan, R.S.
1991-10-01
We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)
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A statistical model for mapping morphological shape
Directory of Open Access Journals (Sweden)
Li Jiahan
2010-07-01
Full Text Available Abstract Background Living things come in all shapes and sizes, from bacteria, plants, and animals to humans. Knowledge about the genetic mechanisms for biological shape has far-reaching implications for a range spectrum of scientific disciplines including anthropology, agriculture, developmental biology, evolution and biomedicine. Results We derived a statistical model for mapping specific genes or quantitative trait loci (QTLs that control morphological shape. The model was formulated within the mixture framework, in which different types of shape are thought to result from genotypic discrepancies at a QTL. The EM algorithm was implemented to estimate QTL genotype-specific shapes based on a shape correspondence analysis. Computer simulation was used to investigate the statistical property of the model. Conclusion By identifying specific QTLs for morphological shape, the model developed will help to ask, disseminate and address many major integrative biological and genetic questions and challenges in the genetic control of biological shape and function.
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Dose reconstruction modeling for medical radiation workers
International Nuclear Information System (INIS)
Choi, Yeong Chull; Cha, Eun Shil; Lee, Won Jin
2017-01-01
Exposure information is a crucial element for the assessment of health risk due to radiation. Radiation doses received by medical radiation workers have been collected and maintained by public registry since 1996. Since exposure levels in the remote past are greater concern, it is essential to reconstruct unmeasured doses in the past using known information. We developed retrodiction models for different groups of medical radiation workers and estimate individual past doses before 1996. Reconstruction models for past radiation doses received by medical radiation workers were developed, and the past doses were estimated. Using these estimates, organ doses should be calculated which, in turn, will be used to explore a wide range of health risks of medical occupational radiation exposure. Reconstruction models for past radiation doses received by medical radiation workers were developed, and the past doses were estimated. Using these estimates, organ doses should be calculated which, in turn, will be used to explore a wide range of health risks of medical occupational radiation exposure.
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Dose reconstruction modeling for medical radiation workers
Energy Technology Data Exchange (ETDEWEB)
Choi, Yeong Chull; Cha, Eun Shil; Lee, Won Jin [Dept. of Preventive Medicine, Korea University, Seoul (Korea, Republic of)
2017-04-15
Exposure information is a crucial element for the assessment of health risk due to radiation. Radiation doses received by medical radiation workers have been collected and maintained by public registry since 1996. Since exposure levels in the remote past are greater concern, it is essential to reconstruct unmeasured doses in the past using known information. We developed retrodiction models for different groups of medical radiation workers and estimate individual past doses before 1996. Reconstruction models for past radiation doses received by medical radiation workers were developed, and the past doses were estimated. Using these estimates, organ doses should be calculated which, in turn, will be used to explore a wide range of health risks of medical occupational radiation exposure. Reconstruction models for past radiation doses received by medical radiation workers were developed, and the past doses were estimated. Using these estimates, organ doses should be calculated which, in turn, will be used to explore a wide range of health risks of medical occupational radiation exposure.
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Statistical model selection with “Big Data”
Directory of Open Access Journals (Sweden)
Jurgen A. Doornik
2015-12-01
Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.
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Statistical shape and appearance models of bones.
Sarkalkan, Nazli; Weinans, Harrie; Zadpoor, Amir A
2014-03-01
When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone. Copyright © 2013 Elsevier Inc. All rights reserved.
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Statistical models and NMR analysis of polymer microstructure
Statistical models can be used in conjunction with NMR spectroscopy to study polymer microstructure and polymerization mechanisms. Thus, Bernoullian, Markovian, and enantiomorphic-site models are well known. Many additional models have been formulated over the years for additional situations. Typica...
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Workshop on Model Uncertainty and its Statistical Implications
1988-01-01
In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.
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Organizational models of educational technology in U.S. and Canadian medical schools.
Souza, Kevin H; Kamin, Carol; O'Sullivan, Patricia; Moses, Anna; Heestand, Diane
2008-07-01
To examine the organizational structure of educational technology units within U.S. and Canadian medical schools in order to (1) identify organization models that support educational technology, (2) describe key attributes of these models, and (3) discuss the strengths and challenges associated with these models. The authors distributed a survey to 88 schools that had previously provided information on their educational technology services and infrastructure. The authors developed the survey through a series of pilots and, then, from the data for each respondent school, created concept maps, which were used to identify organizational models. The authors conducted analyses to determine differences among models. The authors coded the comments about organizational models and identified themes. The authors received adequate data for analysis from 61 schools (69%). Four models for educational technology organizations emerged: (1) centralized units located in the school of medicine, (2) centralized units located at the health science center, (3) centralized units at the comprehensive university, and (4) no centralized unit (Dispersed Model). The majority (75%) of schools relied on some type of centralized organization. Whereas few organization attributes proved to be statistically significant, the centralized models have more resources devoted to educational technology and a closer alignment with the academic mission than the Dispersed Model. Medical schools primarily use central models. The authors recommend that schools structuring their educational technology resources consider exploration of a central model because of its focused use of resources to improve teaching and learning.
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Directory of Open Access Journals (Sweden)
H Zare Ahmadabadi
2007-07-01
Full Text Available Introduction: Many organizations, especially service oriented ones, relative to their goals and mission, have a special view towards quality phenomena and its management. Methods: This paper analyzes medical service quality in one case; The internal section of Shaheed Rahnemoon Hospital Based on the basis of gap analysis model and Servqual technique. A questionnaire was designed and applied to measure expectations and perceptions of patients and personnel of the hospital. Results: On application of non-parametric statistical tests, we propose certain recommendations. These tests drive on five conceptual dimensions of service quality including intangibility, responsiveness, reliability, assurance and empathy. Results show that patients in this section were satisfied from the service provider’s responsiveness, but there are significant differences between expectations and perceptions in other dimensions. Conclusion: The service quality analysis models are useful for managers of medical centers to distinguish gaps between the two sides of service representation; patients and medical centers personnel. Ultimately, they can reinforce strengths and control weaknesses.
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Kolmogorov complexity, pseudorandom generators and statistical models testing
Czech Academy of Sciences Publication Activity Database
Šindelář, Jan; Boček, Pavel
2002-01-01
Roč. 38, č. 6 (2002), s. 747-759 ISSN 0023-5954 R&D Projects: GA ČR GA102/99/1564 Institutional research plan: CEZ:AV0Z1075907 Keywords : Kolmogorov complexity * pseudorandom generators * statistical models testing Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.341, year: 2002
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Applied systems ecology: models, data, and statistical methods
Energy Technology Data Exchange (ETDEWEB)
Eberhardt, L L
1976-01-01
In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.
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Butler, Doug; Bauman, David; Johnson-Throop, Kathy
2011-01-01
The Integrated Medical Model (IMM) Project has been developing a probabilistic risk assessment tool, the IMM, to help evaluate in-flight crew health needs and impacts to the mission due to medical events. This package is a follow-up to a data package provided in June 2009. The IMM currently represents 83 medical conditions and associated ISS resources required to mitigate medical events. IMM end state forecasts relevant to the ISS PRA model include evacuation (EVAC) and loss of crew life (LOCL). The current version of the IMM provides the basis for the operational version of IMM expected in the January 2011 timeframe. The objectives of this data package are: 1. To provide a preliminary understanding of medical risk data used to update the ISS PRA Model. The IMM has had limited validation and an initial characterization of maturity has been completed using NASA STD 7009 Standard for Models and Simulation. The IMM has been internally validated by IMM personnel but has not been validated by an independent body external to the IMM Project. 2. To support a continued dialogue between the ISS PRA and IMM teams. To ensure accurate data interpretation, and that IMM output format and content meets the needs of the ISS Risk Management Office and ISS PRA Model, periodic discussions are anticipated between the risk teams. 3. To help assess the differences between the current ISS PRA and IMM medical risk forecasts of EVAC and LOCL. Follow-on activities are anticipated based on the differences between the current ISS PRA medical risk data and the latest medical risk data produced by IMM.
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The use of statistical models in heavy-ion reactions studies
International Nuclear Information System (INIS)
Stokstad, R.G.
1984-01-01
This chapter reviews the use of statistical models to describe nuclear level densities and the decay of equilibrated nuclei. The statistical models of nuclear structure and nuclear reactions presented here have wide application in the analysis of heavy-ion reaction data. Applications are illustrated with examples of gamma-ray decay, the emission of light particles and heavier clusters of nucleons, and fission. In addition to the compound nucleus, the treatment of equilibrated fragments formed in binary reactions is discussed. The statistical model is shown to be an important tool for the identification of products from nonequilibrium decay
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Sleep Disruption Medical Intervention Forecasting (SDMIF) Module for the Integrated Medical Model
Lewandowski, Beth; Brooker, John; Mallis, Melissa; Hursh, Steve; Caldwell, Lynn; Myers, Jerry
2011-01-01
The NASA Integrated Medical Model (IMM) assesses the risk, including likelihood and impact of occurrence, of all credible in-flight medical conditions. Fatigue due to sleep disruption is a condition that could lead to operational errors, potentially resulting in loss of mission or crew. Pharmacological consumables are mitigation strategies used to manage the risks associated with sleep deficits. The likelihood of medical intervention due to sleep disruption was estimated with a well validated sleep model and a Monte Carlo computer simulation in an effort to optimize the quantity of consumables. METHODS: The key components of the model are the mission parameter program, the calculation of sleep intensity and the diagnosis and decision module. The mission parameter program was used to create simulated daily sleep/wake schedules for an ISS increment. The hypothetical schedules included critical events such as dockings and extravehicular activities and included actual sleep time and sleep quality. The schedules were used as inputs to the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) Model (IBR Inc., Baltimore MD), which calculated sleep intensity. Sleep data from an ISS study was used to relate calculated sleep intensity to the probability of sleep medication use, using a generalized linear model for binomial regression. A human yes/no decision process using a binomial random number was also factored into sleep medication use probability. RESULTS: These probability calculations were repeated 5000 times resulting in an estimate of the most likely amount of sleep aids used during an ISS mission and a 95% confidence interval. CONCLUSIONS: These results were transferred to the parent IMM for further weighting and integration with other medical conditions, to help inform operational decisions. This model is a potential planning tool for ensuring adequate sleep during sleep disrupted periods of a mission.
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Multivariate statistical modelling based on generalized linear models
Fahrmeir, Ludwig
1994-01-01
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...
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Linear mixed models a practical guide using statistical software
West, Brady T; Galecki, Andrzej T
2006-01-01
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo
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The migraine ACE model: evaluating the impact on time lost and medical resource Use.
Caro, J J; Caro, G; Getsios, D; Raggio, G; Burrows, M; Black, L
2000-04-01
To describe the Migraine Adaptive Cost-Effectiveness Model in the context of an analysis of a simulated population of Canadian patients with migraine. The high prevalence of migraine and its substantial impact on patients' ability to function normally present a significant economic burden to society. In light of the recent availability of improved pharmaceutical treatments, a model was developed to assess their economic impact. The Migraine Adaptive Cost-Effectiveness Model incorporates the costs of time lost from both work and nonwork activities, as well as medical resource and medication use. Using Monte Carlo techniques, the model simulates the experience of a population of patients with migraine over the course of 1 year. As an example, analyses of a Canadian population were carried out using data from a multinational trial, surveys, national statistics, and the available literature. Using customary therapy, mean productivity losses (amounting to 84 hours of paid work time, 48 hours of unpaid work time, and 113 hours of leisure time lost) were estimated to cost $1949 (in 1997 Canadian dollars) per patient, with medical expenditures adding an average of $280 to the cost of illness. With customary treatment patterns, the costs of migraine associated with reduced functional capacity are substantial. The migraine model represents a flexible tool for the economic evaluation of different migraine treatments in various populations.
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Active Learning with Statistical Models.
1995-01-01
Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with
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Parametric analysis of the statistical model of the stick-slip process
Lima, Roberta; Sampaio, Rubens
2017-06-01
In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.
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Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams
Directory of Open Access Journals (Sweden)
Chongshi Gu
2013-01-01
Full Text Available Cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Using the parametric statistical model of safety monitoring of cracks in concrete dams, with the help of the semi-parametric statistical theory, and considering the abnormal behaviors of these cracks, the semi-parametric statistical model of safety monitoring of concrete dam cracks is established to overcome the limitation of the parametric model in expressing the objective model. Previous projects show that the semi-parametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semi-parametric statistical model is equivalent to that of the parametric statistical model. The modeling of the semi-parametric statistical model is simple, has a reasonable principle, and has a strong practicality, with a good application prospect in the actual project.
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Statistical models for competing risk analysis
International Nuclear Information System (INIS)
Sather, H.N.
1976-08-01
Research results on three new models for potential applications in competing risks problems. One section covers the basic statistical relationships underlying the subsequent competing risks model development. Another discusses the problem of comparing cause-specific risk structure by competing risks theory in two homogeneous populations, P1 and P2. Weibull models which allow more generality than the Berkson and Elveback models are studied for the effect of time on the hazard function. The use of concomitant information for modeling single-risk survival is extended to the multiple failure mode domain of competing risks. The model used to illustrate the use of this methodology is a life table model which has constant hazards within pre-designated intervals of the time scale. Two parametric models for bivariate dependent competing risks, which provide interesting alternatives, are proposed and examined
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Methods of Medical Guidelines Modelling in GLIF.
Czech Academy of Sciences Publication Activity Database
Buchtela, David; Anger, Z.; Peleška, Jan (ed.); Tomečková, Marie; Veselý, Arnošt; Zvárová, Jana
2005-01-01
Roč. 11, - (2005), s. 1529-1532 ISSN 1727-1983. [EMBEC'05. European Medical and Biomedical Conference /3./. Prague, 20.11.2005-25.11.2005] Institutional research plan: CEZ:AV0Z10300504 Keywords : medical guidelines * knowledge modelling * GLIF model Subject RIV: BD - Theory of Information
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SoS contract verification using statistical model checking
Directory of Open Access Journals (Sweden)
Alessandro Mignogna
2013-11-01
Full Text Available Exhaustive formal verification for systems of systems (SoS is impractical and cannot be applied on a large scale. In this paper we propose to use statistical model checking for efficient verification of SoS. We address three relevant aspects for systems of systems: 1 the model of the SoS, which includes stochastic aspects; 2 the formalization of the SoS requirements in the form of contracts; 3 the tool-chain to support statistical model checking for SoS. We adapt the SMC technique for application to heterogeneous SoS. We extend the UPDM/SysML specification language to express the SoS requirements that the implemented strategies over the SoS must satisfy. The requirements are specified with a new contract language specifically designed for SoS, targeting a high-level English- pattern language, but relying on an accurate semantics given by the standard temporal logics. The contracts are verified against the UPDM/SysML specification using the Statistical Model Checker (SMC PLASMA combined with the simulation engine DESYRE, which integrates heterogeneous behavioral models through the functional mock-up interface (FMI standard. The tool-chain allows computing an estimation of the satisfiability of the contracts by the SoS. The results help the system architect to trade-off different solutions to guide the evolution of the SoS.
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Heard, Kennon; Cleveland, Nathan R; Krier, Shay
2011-11-01
There are no controlled human studies to determine the efficacy of benzodiazepines or antipsychotic medications for prevention or treatment of acute cocaine toxicity. The only available controlled data are from animal models and these studies have reported inconsistent benefits. The objective of this study was to quantify the reported efficacy of benzodiazepines and antipsychotic medication for the prevention of mortality due to cocaine poisoning. We conducted a systematic review to identify English language articles describing experiments that compared a benzodiazepine or antipsychotic medication to placebo for the prevention of acute cocaine toxicity in an animal model. We then used these articles in a meta-analysis with a random-effects model to quantify the absolute risk reduction observed in these experiments. We found 10 articles evaluating antipsychotic medications and 15 articles evaluating benzodiazepines. Antipsychotic medications reduced the risk of death by 27% (95% CI, 15.2%-38.7%) compared to placebo and benzodiazepines reduced the risk of death by 52% (42.8%-60.7%) compared to placebo. Both treatments showed evidence of a dose-response effect, and no experiment found a statistically significant increase in risk of death. We conclude that both benzodiazepines and antipsychotic medications are effective for the prevention of lethality from cocaine toxicity in animal models.
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Karimi, Davood; Samei, Golnoosh; Kesch, Claudia; Nir, Guy; Salcudean, Septimiu E
2018-05-15
Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.
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A statistical model for porous structure of rocks
Institute of Scientific and Technical Information of China (English)
JU Yang; YANG YongMing; SONG ZhenDuo; XU WenJing
2008-01-01
The geometric features and the distribution properties of pores in rocks were In-vestigated by means of CT scanning tests of sandstones. The centroidal coordl-nares of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob-ability density functions upon which the random distribution of pore position, dis-tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex-amine the stress distribution, the pattern of element failure and the inoaculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.
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A statistical model for porous structure of rocks
Institute of Scientific and Technical Information of China (English)
2008-01-01
The geometric features and the distribution properties of pores in rocks were in- vestigated by means of CT scanning tests of sandstones. The centroidal coordi- nates of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and prob- ability density functions upon which the random distribution of pore position, dis- tance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to ex- amine the stress distribution, the pattern of element failure and the inosculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.
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Gooya, Ali; Lekadir, Karim; Alba, Xenia; Swift, Andrew J; Wild, Jim M; Frangi, Alejandro F
2015-01-01
Construction of Statistical Shape Models (SSMs) from arbitrary point sets is a challenging problem due to significant shape variation and lack of explicit point correspondence across the training data set. In medical imaging, point sets can generally represent different shape classes that span healthy and pathological exemplars. In such cases, the constructed SSM may not generalize well, largely because the probability density function (pdf) of the point sets deviates from the underlying assumption of Gaussian statistics. To this end, we propose a generative model for unsupervised learning of the pdf of point sets as a mixture of distinctive classes. A Variational Bayesian (VB) method is proposed for making joint inferences on the labels of point sets, and the principal modes of variations in each cluster. The method provides a flexible framework to handle point sets with no explicit point-to-point correspondences. We also show that by maximizing the marginalized likelihood of the model, the optimal number of clusters of point sets can be determined. We illustrate this work in the context of understanding the anatomical phenotype of the left and right ventricles in heart. To this end, we use a database containing hearts of healthy subjects, patients with Pulmonary Hypertension (PH), and patients with Hypertrophic Cardiomyopathy (HCM). We demonstrate that our method can outperform traditional PCA in both generalization and specificity measures.
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(ajst) statistical mechanics model for orientational
African Journals Online (AJOL)
Science and Engineering Series Vol. 6, No. 2, pp. 94 - 101. STATISTICAL MECHANICS MODEL FOR ORIENTATIONAL. MOTION OF TWO-DIMENSIONAL RIGID ROTATOR. Malo, J.O. ... there is no translational motion and that they are well separated so .... constant and I is the moment of inertia of a linear rotator. Thus, the ...
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Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
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A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects
Directory of Open Access Journals (Sweden)
Shuai Luo
2016-02-01
Full Text Available Bioelectrochemical systems (BES are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.
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Validation of statistical models for creep rupture by parametric analysis
Energy Technology Data Exchange (ETDEWEB)
Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)
2012-01-15
Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).
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Statistical modelling of transcript profiles of differentially regulated genes
Directory of Open Access Journals (Sweden)
Sergeant Martin J
2008-07-01
Full Text Available Abstract Background The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t = A + (B + CtRt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data
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Postdependent state in rats as a model for medication development in alcoholism.
Meinhardt, Marcus W; Sommer, Wolfgang H
2015-01-01
Rational development of novel therapeutic strategies for alcoholism requires understanding of its underlying neurobiology and pathophysiology. Obtaining this knowledge largely relies on animal studies. Thus, choosing the appropriate animal model is one of the most critical steps in pre-clinical medication development. Among the range of animal models that have been used to investigate excessive alcohol consumption in rodents, the postdependent model stands out. It was specifically developed to test the role of negative affect as a key driving force in a perpetuating addiction cycle for alcoholism. Here, we will describe our approach to make rats dependent via chronic intermittent exposure to alcohol, discuss the validity of this model, and compare it with other commonly used animal models of alcoholism. We will summarize evidence that postdependent rats fulfill several criteria of a 'Diagnostic and Statistical Manual of Mental Disorders IV/V-like' diagnostic system. Importantly, these animals show long-lasting excessive consumption of and increased motivation for alcohol, and evidence for loss of control over alcohol intake. Our conclusion that postdependent rats are an excellent model for medication development for alcoholism is underscored by a summary of more than two dozen pharmacological tests aimed at reversing these abnormal alcohol responses. We will end with open questions on the use of this model. In the tradition of the Sanchis-Segura and Spanagel review, we provide comic strips that illustrate the postdependent procedure and relevant phenotypes in this review. © 2014 Society for the Study of Addiction.
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Directory of Open Access Journals (Sweden)
Suo-Wei Wu
2018-01-01
Conclusions: As the two-round questionnaire survey of experts and statistical analysis were performed and credibility of the results was verified through consistency evaluation test, the study established a quantitative medical technology evaluation system model and assessment indicators within medical institutions based on the Delphi method and analytical hierarchy process. Moreover, further verifications, adjustments, and optimizations of the system and indicators will be performed in follow-up studies.
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Acceleration transforms and statistical kinetic models
International Nuclear Information System (INIS)
LuValle, M.J.; Welsher, T.L.; Svoboda, K.
1988-01-01
For a restricted class of problems a mathematical model of microscopic degradation processes, statistical kinetics, is developed and linked through acceleration transforms to the information which can be obtained from a system in which the only observable sign of degradation is sudden and catastrophic failure. The acceleration transforms were developed in accelerated life testing applications as a tool for extrapolating from the observable results of an accelerated life test to the dynamics of the underlying degradation processes. A particular concern of a physicist attempting to interpreted the results of an analysis based on acceleration transforms is determining the physical species involved in the degradation process. These species may be (a) relatively abundant or (b) relatively rare. The main results of this paper are a theorem showing that for an important subclass of statistical kinetic models, acceleration transforms cannot be used to distinguish between cases a and b, and an example showing that in some cases falling outside the restrictions of the theorem, cases a and b can be distinguished by their acceleration transforms
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Statistical models describing the energy signature of buildings
DEFF Research Database (Denmark)
Bacher, Peder; Madsen, Henrik; Thavlov, Anders
2010-01-01
Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i...... or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA......-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i...
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Cui, Wenchao; Wang, Yi; Lei, Tao; Fan, Yangyu; Feng, Yan
2013-01-01
This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.
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STATISTICAL MODELS OF REPRESENTING INTELLECTUAL CAPITAL
Directory of Open Access Journals (Sweden)
Andreea Feraru
2016-06-01
Full Text Available This article entitled Statistical Models of Representing Intellectual Capital approaches and analyses the concept of intellectual capital, as well as the main models which can support enterprisers/managers in evaluating and quantifying the advantages of intellectual capital. Most authors examine intellectual capital from a static perspective and focus on the development of its various evaluation models. In this chapter we surveyed the classical static models: Sveiby, Edvisson, Balanced Scorecard, as well as the canonical model of intellectual capital. Among the group of static models for evaluating organisational intellectual capital the canonical model stands out. This model enables the structuring of organisational intellectual capital in: human capital, structural capital and relational capital. Although the model is widely spread, it is a static one and can thus create a series of errors in the process of evaluation, because all the three entities mentioned above are not independent from the viewpoint of their contents, as any logic of structuring complex entities requires.
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Statistical mechanics of directed models of polymers in the square lattice
Rensburg, J V
2003-01-01
Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce...
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The epistemology of mathematical and statistical modeling: a quiet methodological revolution.
Rodgers, Joseph Lee
2010-01-01
A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models. Copyrigiht 2009 APA, all rights reserved.
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Directory of Open Access Journals (Sweden)
Zvi H. Perry
2014-01-01
Full Text Available Background. We changed the biostatistics curriculum for our medical students and have created a course entitled “Multivariate analysis of statistical data, using the SPSS package.” Purposes. The aim of this course was to develop students’ skills in computerized data analysis, as well as enhancing their ability to read and interpret statistical data analysis in the literature. Methods. In the current study we have shown that a computer-based course for biostatistics and advanced data analysis is feasible and efficient, using course specific evaluation questionnaires. Results. Its efficacy is both subjective (our subjects felt better prepared to do their theses, as well as to read articles with advanced statistical data analysis and objective (their knowledge of how and when to apply statistical procedures seemed to improve. Conclusions. We showed that a formal evaluative process for such a course is possible and that it enhances the learning experience both for the students and their teachers. In the current study we have shown that a computer-based course for biostatistics and advanced data analysis is feasible and efficient.
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Establishing statistical models of manufacturing parameters
International Nuclear Information System (INIS)
Senevat, J.; Pape, J.L.; Deshayes, J.F.
1991-01-01
This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature
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Statistical reconstruction for cosmic ray muon tomography.
Schultz, Larry J; Blanpied, Gary S; Borozdin, Konstantin N; Fraser, Andrew M; Hengartner, Nicolas W; Klimenko, Alexei V; Morris, Christopher L; Orum, Chris; Sossong, Michael J
2007-08-01
Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm2 per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work [1]-[3], we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algorithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.
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"No-fault" compensation for victims of medical injuries. Ten years of implementing the French model.
Barbot, Janine; Parizot, Isabelle; Winance, Myriam
2014-02-01
For decades and in many countries, the issue of compensation for victims of medical injuries has led to lively debates. In 2002, a law set up a new model for compensation in France - based on the creation of a "no fault" compensation scheme and of an out-of-court settlement mechanism. This is one of the most recent models to have been adopted in European countries. This article analyses the choices made by the law and discusses the key figures of its ten years of implementation. We conducted (1) a study of debates regarding compensation for victims of medical injuries in France; (2) a comparative analysis of the different models of compensation which had already been adopted in different countries; (3) a study of primary sources provided by the bodies in charge of the French new out-of-court settlement mechanism; and (4) a statistical analysis of the exhaustive list of 18,258 claims filed between 2003 and 2009. The article highlights the context which led to the adoption of the 2002 law on the quality of care and patients' rights. It analyses, from a comparative standpoint, the specificities of the new compensation model set up by the law. It shows how the opportunities for victims of medical injuries to be compensated had improved in France. Finally, we discuss the limits of the new model and what the next step might be to improve access to compensation for victims of medical injuries. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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Statistical geological discrete fracture network model. Forsmark modelling stage 2.2
Energy Technology Data Exchange (ETDEWEB)
Fox, Aaron; La Pointe, Paul [Golder Associates Inc (United States); Simeonov, Assen [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden); Hermanson, Jan; Oehman, Johan [Golder Associates AB, Stockholm (Sweden)
2007-11-15
The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions
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Statistical geological discrete fracture network model. Forsmark modelling stage 2.2
International Nuclear Information System (INIS)
Fox, Aaron; La Pointe, Paul; Simeonov, Assen; Hermanson, Jan; Oehman, Johan
2007-11-01
The Swedish Nuclear Fuel and Waste Management Company (SKB) is performing site characterization at two different locations, Forsmark and Laxemar, in order to locate a site for a final geologic repository for spent nuclear fuel. The program is built upon the development of Site Descriptive Models (SDMs) at specific timed data freezes. Each SDM is formed from discipline-specific reports from across the scientific spectrum. This report describes the methods, analyses, and conclusions of the geological modeling team with respect to a geological and statistical model of fractures and minor deformation zones (henceforth referred to as the geological DFN), version 2.2, at the Forsmark site. The geological DFN builds upon the work of other geological modelers, including the deformation zone (DZ), rock domain (RD), and fracture domain (FD) models. The geological DFN is a statistical model for stochastically simulating rock fractures and minor deformation zones as a scale of less than 1,000 m (the lower cut-off of the DZ models). The geological DFN is valid within four specific fracture domains inside the local model region, and encompassing the candidate volume at Forsmark: FFM01, FFM02, FFM03, and FFM06. The models are build using data from detailed surface outcrop maps and the cored borehole record at Forsmark. The conceptual model for the Forsmark 2.2 geological revolves around the concept of orientation sets; for each fracture domain, other model parameters such as size and intensity are tied to the orientation sets. Two classes of orientation sets were described; Global sets, which are encountered everywhere in the model region, and Local sets, which represent highly localized stress environments. Orientation sets were described in terms of their general cardinal direction (NE, NW, etc). Two alternatives are presented for fracture size modeling: - the tectonic continuum approach (TCM, TCMF) described by coupled size-intensity scaling following power law distributions
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Statistical Modelling of the Soil Dielectric Constant
Usowicz, Boguslaw; Marczewski, Wojciech; Bogdan Usowicz, Jerzy; Lipiec, Jerzy
2010-05-01
The dielectric constant of soil is the physical property being very sensitive on water content. It funds several electrical measurement techniques for determining the water content by means of direct (TDR, FDR, and others related to effects of electrical conductance and/or capacitance) and indirect RS (Remote Sensing) methods. The work is devoted to a particular statistical manner of modelling the dielectric constant as the property accounting a wide range of specific soil composition, porosity, and mass density, within the unsaturated water content. Usually, similar models are determined for few particular soil types, and changing the soil type one needs switching the model on another type or to adjust it by parametrization of soil compounds. Therefore, it is difficult comparing and referring results between models. The presented model was developed for a generic representation of soil being a hypothetical mixture of spheres, each representing a soil fraction, in its proper phase state. The model generates a serial-parallel mesh of conductive and capacitive paths, which is analysed for a total conductive or capacitive property. The model was firstly developed to determine the thermal conductivity property, and now it is extended on the dielectric constant by analysing the capacitive mesh. The analysis is provided by statistical means obeying physical laws related to the serial-parallel branching of the representative electrical mesh. Physical relevance of the analysis is established electrically, but the definition of the electrical mesh is controlled statistically by parametrization of compound fractions, by determining the number of representative spheres per unitary volume per fraction, and by determining the number of fractions. That way the model is capable covering properties of nearly all possible soil types, all phase states within recognition of the Lorenz and Knudsen conditions. In effect the model allows on generating a hypothetical representative of
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Chung, Moo K.; Kim, Seung-Goo; Schaefer, Stacey M.; van Reekum, Carien M.; Peschke-Schmitz, Lara; Sutterer, Matthew J.; Davidson, Richard J.
2014-03-01
The sparse regression framework has been widely used in medical image processing and analysis. However, it has been rarely used in anatomical studies. We present a sparse shape modeling framework using the Laplace- Beltrami (LB) eigenfunctions of the underlying shape and show its improvement of statistical power. Tradition- ally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we present a LB-based method to filter out only the significant eigenfunctions by imposing a sparse penalty. For dense anatomical data such as deformation fields on a surface mesh, the sparse regression behaves like a smoothing process, which will reduce the error of incorrectly detecting false negatives. Hence the statistical power improves. The sparse shape model is then applied in investigating the influence of age on amygdala and hippocampus shapes in the normal population. The advantage of the LB sparse framework is demonstrated by showing the increased statistical power.
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Narayanan, Roshni; Nugent, Rebecca; Nugent, Kenneth
2015-10-01
Accreditation Council for Graduate Medical Education guidelines require internal medicine residents to develop skills in the interpretation of medical literature and to understand the principles of research. A necessary component is the ability to understand the statistical methods used and their results, material that is not an in-depth focus of most medical school curricula and residency programs. Given the breadth and depth of the current medical literature and an increasing emphasis on complex, sophisticated statistical analyses, the statistical foundation and education necessary for residents are uncertain. We reviewed the statistical methods and terms used in 49 articles discussed at the journal club in the Department of Internal Medicine residency program at Texas Tech University between January 1, 2013 and June 30, 2013. We collected information on the study type and on the statistical methods used for summarizing and comparing samples, determining the relations between independent variables and dependent variables, and estimating models. We then identified the typical statistics education level at which each term or method is learned. A total of 14 articles came from the Journal of the American Medical Association Internal Medicine, 11 from the New England Journal of Medicine, 6 from the Annals of Internal Medicine, 5 from the Journal of the American Medical Association, and 13 from other journals. Twenty reported randomized controlled trials. Summary statistics included mean values (39 articles), category counts (38), and medians (28). Group comparisons were based on t tests (14 articles), χ2 tests (21), and nonparametric ranking tests (10). The relations between dependent and independent variables were analyzed with simple regression (6 articles), multivariate regression (11), and logistic regression (8). Nine studies reported odds ratios with 95% confidence intervals, and seven analyzed test performance using sensitivity and specificity calculations
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Qi, D.; Majda, A.
2017-12-01
A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with
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Bayesian models based on test statistics for multiple hypothesis testing problems.
Ji, Yuan; Lu, Yiling; Mills, Gordon B
2008-04-01
We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.
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On-the-fly confluence detection for statistical model checking (extended version)
Hartmanns, Arnd; Timmer, Mark
Statistical model checking is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can only provide sound results if the
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Topology for Statistical Modeling of Petascale Data
Energy Technology Data Exchange (ETDEWEB)
Bennett, Janine Camille [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pebay, Philippe Pierre [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Rojas, Maurice [Texas A & M Univ., College Station, TX (United States)
2014-07-01
This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled "Topology for Statistical Modeling of Petascale Data", funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program.
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Brain Aneurysm Statistics and Facts
... Statistics and Facts A- A A+ Brain Aneurysm Statistics and Facts An estimated 6 million people in ... Understanding the Brain Warning Signs/ Symptoms Brain Aneurysm Statistics and Facts Seeking Medical Attention Risk Factors Aneurysm ...
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Analyzing sickness absence with statistical models for survival data
DEFF Research Database (Denmark)
Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars
2007-01-01
OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation...
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Beam, Andrew L.; Kartoun, Uri; Pai, Jennifer K.; Chatterjee, Arnaub K.; Fitzgerald, Timothy P.; Shaw, Stanley Y.; Kohane, Isaac S.
2017-02-01
Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13 p = 3 × 10-37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient’s note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.
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Commentary: discovering a different model of medical student education.
Watson, Robert T
2012-12-01
Traditional medical schools in modern academic health centers make discoveries, create new knowledge and technology, provide innovative care to the sickest patients, and educate future academic and practicing physicians. Unfortunately, the growth of the research and clinical care missions has sometimes resulted in a loss of emphasis on the general professional education of medical students. The author concludes that it may not be practical for many established medical schools to functionally return to the reason they were created: for the education of medical students.He had the opportunity to discover a different model of medical student education at the first new MD-granting medical school created in the United States in 25 years (in 2000), the Florida State University College of Medicine. He was initially skeptical about how its distributed regional campuses model, using practicing primary care physicians to help medical students learn in mainly ambulatory settings, could be effective. But his experience as a faculty member at the school convinced him that the model works very well.He proposes a better alignment of form and function for many established medical schools and an extension of the regional community-based model to the formation of community-based primary care graduate medical education programs determined by physician workforce needs and available resources.
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Physics-based statistical model and simulation method of RF propagation in urban environments
Pao, Hsueh-Yuan; Dvorak, Steven L.
2010-09-14
A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.
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Austin, Peter C; Reeves, Mathew J
2013-03-01
Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card.
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Encoding Dissimilarity Data for Statistical Model Building.
Wahba, Grace
2010-12-01
We summarize, review and comment upon three papers which discuss the use of discrete, noisy, incomplete, scattered pairwise dissimilarity data in statistical model building. Convex cone optimization codes are used to embed the objects into a Euclidean space which respects the dissimilarity information while controlling the dimension of the space. A "newbie" algorithm is provided for embedding new objects into this space. This allows the dissimilarity information to be incorporated into a Smoothing Spline ANOVA penalized likelihood model, a Support Vector Machine, or any model that will admit Reproducing Kernel Hilbert Space components, for nonparametric regression, supervised learning, or semi-supervised learning. Future work and open questions are discussed. The papers are: F. Lu, S. Keles, S. Wright and G. Wahba 2005. A framework for kernel regularization with application to protein clustering. Proceedings of the National Academy of Sciences 102, 12332-1233.G. Corrada Bravo, G. Wahba, K. Lee, B. Klein, R. Klein and S. Iyengar 2009. Examining the relative influence of familial, genetic and environmental covariate information in flexible risk models. Proceedings of the National Academy of Sciences 106, 8128-8133F. Lu, Y. Lin and G. Wahba. Robust manifold unfolding with kernel regularization. TR 1008, Department of Statistics, University of Wisconsin-Madison.
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Perneger, Thomas V; Combescure, Christophe
2017-07-01
Published P-values provide a window into the global enterprise of medical research. The aim of this study was to use the distribution of published P-values to estimate the relative frequencies of null and alternative hypotheses and to seek irregularities suggestive of publication bias. This cross-sectional study included P-values published in 120 medical research articles in 2016 (30 each from the BMJ, JAMA, Lancet, and New England Journal of Medicine). The observed distribution of P-values was compared with expected distributions under the null hypothesis (i.e., uniform between 0 and 1) and the alternative hypothesis (strictly decreasing from 0 to 1). P-values were categorized according to conventional levels of statistical significance and in one-percent intervals. Among 4,158 recorded P-values, 26.1% were highly significant (P values values equal to 1, and (3) about twice as many P-values less than 0.05 compared with those more than 0.05. The latter finding was seen in both randomized trials and observational studies, and in most types of analyses, excepting heterogeneity tests and interaction tests. Under plausible assumptions, we estimate that about half of the tested hypotheses were null and the other half were alternative. This analysis suggests that statistical tests published in medical journals are not a random sample of null and alternative hypotheses but that selective reporting is prevalent. In particular, significant results are about twice as likely to be reported as nonsignificant results. Copyright © 2017 Elsevier Inc. All rights reserved.
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Simple classical model for Fano statistics in radiation detectors
Energy Technology Data Exchange (ETDEWEB)
Jordan, David V. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)], E-mail: David.Jordan@pnl.gov; Renholds, Andrea S.; Jaffe, John E.; Anderson, Kevin K.; Rene Corrales, L.; Peurrung, Anthony J. [Pacific Northwest National Laboratory, National Security Division - Radiological and Chemical Sciences Group PO Box 999, Richland, WA 99352 (United States)
2008-02-01
A simple classical model that captures the essential statistics of energy partitioning processes involved in the creation of information carriers (ICs) in radiation detectors is presented. The model pictures IC formation from a fixed amount of deposited energy in terms of the statistically analogous process of successively sampling water from a large, finite-volume container ('bathtub') with a small dipping implement ('shot or whiskey glass'). The model exhibits sub-Poisson variance in the distribution of the number of ICs generated (the 'Fano effect'). Elementary statistical analysis of the model clarifies the role of energy conservation in producing the Fano effect and yields Fano's prescription for computing the relative variance of the IC number distribution in terms of the mean and variance of the underlying, single-IC energy distribution. The partitioning model is applied to the development of the impact ionization cascade in semiconductor radiation detectors. It is shown that, in tandem with simple assumptions regarding the distribution of energies required to create an (electron, hole) pair, the model yields an energy-independent Fano factor of 0.083, in accord with the lower end of the range of literature values reported for silicon and high-purity germanium. The utility of this simple picture as a diagnostic tool for guiding or constraining more detailed, 'microscopic' physical models of detector material response to ionizing radiation is discussed.
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Risk prediction model: Statistical and artificial neural network approach
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
2017-04-01
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
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Development of a career coaching model for medical students.
Hur, Yera
2016-03-01
Deciding on a future career path or choosing a career specialty is an important academic decision for medical students. The purpose of this study is to develop a career coaching model for medical students. This research was carried out in three steps. The first step was systematic review of previous studies. The second step was a need assessment of medical students. The third step was a career coaching model using the results acquired from the researched literature and the survey. The career coaching stages were defined as three big phases: The career coaching stages were defined as the "crystallization" period (Pre-medical year 1 and 2), "specification" period (medical year 1 and 2), and "implementation" period (medical year 3 and 4). The career coaching model for medical students can be used in programming career coaching contents and also in identifying the outcomes of career coaching programs at an institutional level.
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Statistical learning modeling method for space debris photometric measurement
Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen
2016-03-01
Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.
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GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science
Caron, L.; Ivins, E. R.; Larour, E.; Adhikari, S.; Nilsson, J.; Blewitt, G.
2018-03-01
We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (≤2 mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7 year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14 years of GRACE data in polar regions.
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Experimental, statistical, and biological models of radon carcinogenesis
International Nuclear Information System (INIS)
Cross, F.T.
1991-09-01
Risk models developed for underground miners have not been consistently validated in studies of populations exposed to indoor radon. Imprecision in risk estimates results principally from differences between exposures in mines as compared to domestic environments and from uncertainties about the interaction between cigarette-smoking and exposure to radon decay products. Uncertainties in extrapolating miner data to domestic exposures can be reduced by means of a broad-based health effects research program that addresses the interrelated issues of exposure, respiratory tract dose, carcinogenesis (molecular/cellular and animal studies, plus developing biological and statistical models), and the relationship of radon to smoking and other copollutant exposures. This article reviews experimental animal data on radon carcinogenesis observed primarily in rats at Pacific Northwest Laboratory. Recent experimental and mechanistic carcinogenesis models of exposures to radon, uranium ore dust, and cigarette smoke are presented with statistical analyses of animal data. 20 refs., 1 fig
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[New business model for medical specialists].
Houwen, L G H J Louis
2013-01-01
The reforms in the field of medical specialist care have important implications for the professional practice of medical specialists and their working relationship with the hospital. This leads to a considerable amount of pressure placed upon the way physicians have traditionally practiced their liberal professions, which is by forming partnerships and practicing from within the hospitals based on an admission agreement. As of 2015, the tax benefits for entrepreneurs will be abolished and the formation of regional partnerships will be discouraged. These developments not only pose threats but also offer opportunities for both the entrepreneurial medical specialist and the innovative hospital. In this article, the prospect of a future business model for specialist medical care will be outlined and explored by proposing three new organizational forms. The central vision of this model is that physicians who wish to retain their status of liberal professional practitioners in the twenty-first century should be more involved in the ownership structure of hospitals. The social importance of responsible patient care remains paramount.
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A statistical model for instable thermodynamical systems
International Nuclear Information System (INIS)
Sommer, Jens-Uwe
2003-01-01
A generic model is presented for statistical systems which display thermodynamic features in contrast to our everyday experience, such as infinite and negative heat capacities. Such system are instable in terms of classical equilibrium thermodynamics. Using our statistical model, we are able to investigate states of instable systems which are undefined in the framework of equilibrium thermodynamics. We show that a region of negative heat capacity in the adiabatic environment, leads to a first order like phase transition when the system is coupled to a heat reservoir. This phase transition takes place without a phase coexistence. Nevertheless, all intermediate states are stable due to fluctuations. When two instable system are brought in thermal contact, the temperature of the composed system is lower than the minimum temperature of the individual systems. Generally, the equilibrium states of instable system cannot be simply decomposed into equilibrium states of the individual systems. The properties of instable system depend on the environment, ensemble equivalence is broken
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Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas
Zhang, Yue; Fu, Kun; Sun, Xian; Xu, Guangluan; Wang, Hongqi
2016-06-01
The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.
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MODEL ACCURACY COMPARISON FOR HIGH RESOLUTION INSAR COHERENCE STATISTICS OVER URBAN AREAS
Directory of Open Access Journals (Sweden)
Y. Zhang
2016-06-01
Full Text Available The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.
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Development of a career coaching model for medical students
Directory of Open Access Journals (Sweden)
Yera Hur
2016-03-01
Full Text Available Purpose: Deciding on a future career path or choosing a career specialty is an important academic decision for medical students. The purpose of this study is to develop a career coaching model for medical students. Methods: This research was carried out in three steps. The first step was systematic review of previous studies. The second step was a need assessment of medical students. The third step was a career coaching model using the results acquired from the researched literature and the survey. Results: The career coaching stages were defined as three big phases: The career coaching stages were defined as the “crystallization” period (Pre-medical year 1 and 2, “specification” period (medical year 1 and 2, and “implementation” period (medical year 3 and 4. Conclusion: The career coaching model for medical students can be used in programming career coaching contents and also in identifying the outcomes of career coaching programs at an institutional level.
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International Nuclear Information System (INIS)
Koyumdjieva, N.
2006-01-01
A statistical model for the resonant cross section structure in the Unresolved Resonance Region has been developed in the framework of the R-matrix formalism in Reich Moore approach with effective accounting of the resonance parameters fluctuations. The model uses only the average resonance parameters and can be effectively applied for analyses of cross sections functional, averaged over many resonances. Those are cross section moments, transmission and self-indication functions measured through thick sample. In this statistical model the resonant cross sections structure is accepted to be periodic and the R-matrix is a function of ε=E/D with period 0≤ε≤N; R nc (ε)=π/2√(S n *S c )1/NΣ(i=1,N)(β in *β ic *ctg[π(ε i - = ε-iS i )/N]; Here S n ,S c ,S i is respectively neutron strength function, strength function for fission or inelastic channel and strength function for radiative capture, N is the number of resonances (ε i ,β i ) that obey the statistic of Porter-Thomas and Wigner's one. The simple case of this statistical model concerns the resonant cross section structure for non-fissile nuclei under the threshold for inelastic scattering - the model of the characteristic function with HARFOR program. In the above model some improvements of calculation of the phases and logarithmic derivatives of neutron channels have been done. In the parameterization we use the free parameter R l ∞ , which accounts the influence of long-distant resonances. The above scheme for statistical modelling of the resonant cross section structure has been applied for evaluation of experimental data for total, capture and inelastic cross sections for 232 Th in the URR (4-150) keV and also the transmission and self-indication functions in (4-175) keV. The set of evaluated average resonance parameters have been obtained. The evaluated average resonance parameters in the URR are consistent with those in the Resolved Resonance Region (CRP for Th-U cycle, Vienna, 2006
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Do doctors need statistics? Doctors' use of and attitudes to probability and statistics.
Swift, Louise; Miles, Susan; Price, Gill M; Shepstone, Lee; Leinster, Sam J
2009-07-10
There is little published evidence on what doctors do in their work that requires probability and statistics, yet the General Medical Council (GMC) requires new doctors to have these skills. This study investigated doctors' use of and attitudes to probability and statistics with a view to informing undergraduate teaching.An email questionnaire was sent to 473 clinicians with an affiliation to the University of East Anglia's Medical School.Of 130 respondents approximately 90 per cent of doctors who performed each of the following activities found probability and statistics useful for that activity: accessing clinical guidelines and evidence summaries, explaining levels of risk to patients, assessing medical marketing and advertising material, interpreting the results of a screening test, reading research publications for general professional interest, and using research publications to explore non-standard treatment and management options.Seventy-nine per cent (103/130, 95 per cent CI 71 per cent, 86 per cent) of participants considered probability and statistics important in their work. Sixty-three per cent (78/124, 95 per cent CI 54 per cent, 71 per cent) said that there were activities that they could do better or start doing if they had an improved understanding of these areas and 74 of these participants elaborated on this. Themes highlighted by participants included: being better able to critically evaluate other people's research; becoming more research-active, having a better understanding of risk; and being better able to explain things to, or teach, other people.Our results can be used to inform how probability and statistics should be taught to medical undergraduates and should encourage today's medical students of the subjects' relevance to their future careers. Copyright 2009 John Wiley & Sons, Ltd.
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Statistical models for expert judgement and wear prediction
International Nuclear Information System (INIS)
Pulkkinen, U.
1994-01-01
This thesis studies the statistical analysis of expert judgements and prediction of wear. The point of view adopted is the one of information theory and Bayesian statistics. A general Bayesian framework for analyzing both the expert judgements and wear prediction is presented. Information theoretic interpretations are given for some averaging techniques used in the determination of consensus distributions. Further, information theoretic models are compared with a Bayesian model. The general Bayesian framework is then applied in analyzing expert judgements based on ordinal comparisons. In this context, the value of information lost in the ordinal comparison process is analyzed by applying decision theoretic concepts. As a generalization of the Bayesian framework, stochastic filtering models for wear prediction are formulated. These models utilize the information from condition monitoring measurements in updating the residual life distribution of mechanical components. Finally, the application of stochastic control models in optimizing operational strategies for inspected components are studied. Monte-Carlo simulation methods, such as the Gibbs sampler and the stochastic quasi-gradient method, are applied in the determination of posterior distributions and in the solution of stochastic optimization problems. (orig.) (57 refs., 7 figs., 1 tab.)
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Security of statistical data bases: invasion of privacy through attribute correlational modeling
Energy Technology Data Exchange (ETDEWEB)
Palley, M.A.
1985-01-01
This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queries of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.
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A no extensive statistical model for the nucleon structure function
International Nuclear Information System (INIS)
Trevisan, Luis A.; Mirez, Carlos
2013-01-01
We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon.
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PVeStA: A Parallel Statistical Model Checking and Quantitative Analysis Tool
AlTurki, Musab
2011-01-01
Statistical model checking is an attractive formal analysis method for probabilistic systems such as, for example, cyber-physical systems which are often probabilistic in nature. This paper is about drastically increasing the scalability of statistical model checking, and making such scalability of analysis available to tools like Maude, where probabilistic systems can be specified at a high level as probabilistic rewrite theories. It presents PVeStA, an extension and parallelization of the VeStA statistical model checking tool [10]. PVeStA supports statistical model checking of probabilistic real-time systems specified as either: (i) discrete or continuous Markov Chains; or (ii) probabilistic rewrite theories in Maude. Furthermore, the properties that it can model check can be expressed in either: (i) PCTL/CSL, or (ii) the QuaTEx quantitative temporal logic. As our experiments show, the performance gains obtained from parallelization can be very high. © 2011 Springer-Verlag.
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2010 Aerospace Medical Certification Statistical Handbook
2012-02-01
from the Aviation Medical Examiner Information System (AMEIS). The current status of each AME was determined for each year of the study period from...severe gastritis, esophageal reflux, achalasia, GERD, gastroplasty, dysphagia , dyspepsia, thrombus abdomen aorta# 21,902 3.66 Other skin
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Inclusion of temperature dependence of fission barriers in statistical model calculations
International Nuclear Information System (INIS)
Newton, J.O.; Popescu, D.G.; Leigh, J.R.
1990-08-01
The temperature dependence of fission barriers has been interpolated from the results of recent theoretical calculations and included in the statistical model code PACE2. It is shown that the inclusion of temperature dependence causes significant changes to the values of the statistical model parameters deduced from fits to experimental data. 21 refs., 2 figs
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Martin, Justin D.
2017-01-01
This essay presents data from a census of statistics requirements and offerings at all 4-year journalism programs in the United States (N = 369) and proposes a model of a potential course in statistics for journalism majors. The author proposes that three philosophies underlie a statistics course for journalism students. Such a course should (a)…
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Central Limit Theorem for Exponentially Quasi-local Statistics of Spin Models on Cayley Graphs
Reddy, Tulasi Ram; Vadlamani, Sreekar; Yogeshwaran, D.
2018-04-01
Central limit theorems for linear statistics of lattice random fields (including spin models) are usually proven under suitable mixing conditions or quasi-associativity. Many interesting examples of spin models do not satisfy mixing conditions, and on the other hand, it does not seem easy to show central limit theorem for local statistics via quasi-associativity. In this work, we prove general central limit theorems for local statistics and exponentially quasi-local statistics of spin models on discrete Cayley graphs with polynomial growth. Further, we supplement these results by proving similar central limit theorems for random fields on discrete Cayley graphs taking values in a countable space, but under the stronger assumptions of α -mixing (for local statistics) and exponential α -mixing (for exponentially quasi-local statistics). All our central limit theorems assume a suitable variance lower bound like many others in the literature. We illustrate our general central limit theorem with specific examples of lattice spin models and statistics arising in computational topology, statistical physics and random networks. Examples of clustering spin models include quasi-associated spin models with fast decaying covariances like the off-critical Ising model, level sets of Gaussian random fields with fast decaying covariances like the massive Gaussian free field and determinantal point processes with fast decaying kernels. Examples of local statistics include intrinsic volumes, face counts, component counts of random cubical complexes while exponentially quasi-local statistics include nearest neighbour distances in spin models and Betti numbers of sub-critical random cubical complexes.
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Structural reliability in context of statistical uncertainties and modelling discrepancies
International Nuclear Information System (INIS)
Pendola, Maurice
2000-01-01
Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr
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Nishino, Ko; Lombardi, Stephen
2011-01-01
We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.
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Statistical approach for selection of regression model during validation of bioanalytical method
Directory of Open Access Journals (Sweden)
Natalija Nakov
2014-06-01
Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.
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Statistical modeling of geopressured geothermal reservoirs
Ansari, Esmail; Hughes, Richard; White, Christopher D.
2017-06-01
Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.
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Dual processing model of medical decision-making
2012-01-01
Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical
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Dual processing model of medical decision-making.
Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G
2012-09-03
Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. We show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the
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Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.
MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C
2018-03-29
This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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texreg: Conversion of Statistical Model Output in R to LATEX and HTML Tables
Directory of Open Access Journals (Sweden)
Philip Leifeld
2013-11-01
Full Text Available A recurrent task in applied statistics is the (mostly manual preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.
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Bayesian statistic methods and theri application in probabilistic simulation models
Directory of Open Access Journals (Sweden)
Sergio Iannazzo
2007-03-01
Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.
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Drake, Richard J; Nordentoft, Merete; Haddock, Gillian; Arango, Celso; Fleischhacker, W Wolfgang; Glenthøj, Birte; Leboyer, Marion; Leucht, Stefan; Leweke, Markus; McGuire, Phillip; Meyer-Lindenberg, Andreas; Rujescu, Dan; Sommer, Iris E; Kahn, René S; Lewis, Shon W
2015-05-01
We aimed to design a multimodal intervention to improve adherence following first episode psychosis, consistent with current evidence. Existing literature identified medication attitudes, insight, and characteristics of support as important determinants of adherence to medication: we examined medication attitudes, self-esteem, and insight in an early psychosis cohort better to understand their relationships. Existing longitudinal data from 309 patients with early Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, nonaffective psychosis (83% first episode) were analyzed to test the hypothesis that medication attitudes, while meaningfully different from "insight," correlated with insight and self-esteem, and change in each influenced the others. Rosenberg Self-Esteem Scale, Birchwood Insight Scale, and Positive and Negative Syndrome Scale insight were assessed at presentation, after 6 weeks and 3 and 18 months. Drug Attitudes Inventory (DAI) and treatment satisfaction were rated from 6 weeks onward. Structural equation models of their relationships were compared. Insight measures' and DAI's predictive validity were compared against relapse, readmission, and remission. Analysis found five latent constructs best fitted the data: medication attitudes, self-esteem, accepting need for treatment, self-rated insight, and objective insight. All were related and each affected the others as it changed, except self-esteem and medication attitudes. Low self-reported insight at presentation predicted readmission. Good 6-week insight (unlike drug attitudes) predicted remission. Literature review and data modeling indicated that a multimodal intervention using motivational interviewing, online psychoeducation, and SMS text medication reminders to enhance adherence without damaging self-concept was feasible and appropriate. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For
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Statistical modelling for recurrent events: an application to sports injuries.
Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F
2014-09-01
Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
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A classical statistical model of heavy ion collisions
International Nuclear Information System (INIS)
Schmidt, R.; Teichert, J.
1980-01-01
The use of the computer code TRAJEC which represents the numerical realization of a classical statistical model for heavy ion collisions is described. The code calculates the results of a classical friction model as well as various multi-differential cross sections for heavy ion collisions. INPUT and OUTPUT information of the code are described. Two examples of data sets are given [ru
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The GNASH preequilibrium-statistical nuclear model code
International Nuclear Information System (INIS)
Arthur, E. D.
1988-01-01
The following report is based on materials presented in a series of lectures at the International Center for Theoretical Physics, Trieste, which were designed to describe the GNASH preequilibrium statistical model code and its use. An overview is provided of the code with emphasis upon code's calculational capabilities and the theoretical models that have been implemented in it. Two sample problems are discussed, the first dealing with neutron reactions on 58 Ni. the second illustrates the fission model capabilities implemented in the code and involves n + 235 U reactions. Finally a description is provided of current theoretical model and code development underway. Examples of calculated results using these new capabilities are also given. 19 refs., 17 figs., 3 tabs
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Yang, Yongji; Moser, Michael A J; Zhang, Edwin; Zhang, Wenjun; Zhang, Bing
2018-01-01
The aim of this study was to develop a statistical model for cell death by irreversible electroporation (IRE) and to show that the statistic model is more accurate than the electric field threshold model in the literature using cervical cancer cells in vitro. HeLa cell line was cultured and treated with different IRE protocols in order to obtain data for modeling the statistical relationship between the cell death and pulse-setting parameters. In total, 340 in vitro experiments were performed with a commercial IRE pulse system, including a pulse generator and an electric cuvette. Trypan blue staining technique was used to evaluate cell death after 4 hours of incubation following IRE treatment. Peleg-Fermi model was used in the study to build the statistical relationship using the cell viability data obtained from the in vitro experiments. A finite element model of IRE for the electric field distribution was also built. Comparison of ablation zones between the statistical model and electric threshold model (drawn from the finite element model) was used to show the accuracy of the proposed statistical model in the description of the ablation zone and its applicability in different pulse-setting parameters. The statistical models describing the relationships between HeLa cell death and pulse length and the number of pulses, respectively, were built. The values of the curve fitting parameters were obtained using the Peleg-Fermi model for the treatment of cervical cancer with IRE. The difference in the ablation zone between the statistical model and the electric threshold model was also illustrated to show the accuracy of the proposed statistical model in the representation of ablation zone in IRE. This study concluded that: (1) the proposed statistical model accurately described the ablation zone of IRE with cervical cancer cells, and was more accurate compared with the electric field model; (2) the proposed statistical model was able to estimate the value of electric
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DEFF Research Database (Denmark)
ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto
2015-01-01
We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting pa...
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Modeling Medical Services with Mobile Health Applications
Directory of Open Access Journals (Sweden)
Zhenfei Wang
2018-01-01
Full Text Available The rapid development of mobile health technology (m-Health provides unprecedented opportunities for improving health services. As the bridge between doctors and patients, mobile health applications enable patients to communicate with doctors through their smartphones, which is becoming more and more popular among people. To evaluate the influence of m-Health applications on the medical service market, we propose a medical service equilibrium model. The model can balance the supply of doctors and demand of patients and reflect possible options for both doctors and patients with or without m-Health applications in the medical service market. In the meantime, we analyze the behavior of patients and the activities of doctors to minimize patients’ full costs of healthcare and doctors’ futility. Then, we provide a resolution algorithm through mathematical reasoning. Lastly, based on artificially generated dataset, experiments are conducted to evaluate the medical services of m-Health applications.
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The matchmaking paradox: a statistical explanation
International Nuclear Information System (INIS)
Eliazar, Iddo I; Sokolov, Igor M
2010-01-01
Medical surveys regarding the number of heterosexual partners per person yield different female and male averages-a result which, from a physical standpoint, is impossible. In this paper we term this puzzle the 'matchmaking paradox', and establish a statistical model explaining it. We consider a bipartite graph with N male and N female nodes (N >> 1), and B bonds connecting them (B >> 1). Each node is associated a random 'attractiveness level', and the bonds connect to the nodes randomly-with probabilities which are proportionate to the nodes' attractiveness levels. The population's average bonds-per-nodes B/N is estimated via a sample average calculated from a survey of size n (n >> 1). A comprehensive statistical analysis of this model is carried out, asserting that (i) the sample average well estimates the population average if and only if the attractiveness levels possess a finite mean; (ii) if the attractiveness levels are governed by a 'fat-tailed' probability law then the sample average displays wild fluctuations and strong skew-thus providing a statistical explanation to the matchmaking paradox.
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The Statistical Modeling of the Trends Concerning the Romanian Population
Directory of Open Access Journals (Sweden)
Gabriela OPAIT
2014-11-01
Full Text Available This paper reflects the statistical modeling concerning the resident population in Romania, respectively the total of the romanian population, through by means of the „Least Squares Method”. Any country it develops by increasing of the population, respectively of the workforce, which is a factor of influence for the growth of the Gross Domestic Product (G.D.P.. The „Least Squares Method” represents a statistical technique for to determine the trend line of the best fit concerning a model.
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Sound statistical model checking for MDP using partial order and confluence reduction
Hartmanns, Arnd; Timmer, Mark
Statistical model checking (SMC) is an analysis method that circumvents the state space explosion problem in model-based verification by combining probabilistic simulation with statistical methods that provide clear error bounds. As a simulation-based technique, it can in general only provide sound
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Statistical modeling to support power system planning
Staid, Andrea
This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today's power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate
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Efficient Parallel Statistical Model Checking of Biochemical Networks
Directory of Open Access Journals (Sweden)
Paolo Ballarini
2009-12-01
Full Text Available We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.
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A Model of Statistics Performance Based on Achievement Goal Theory.
Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.
2003-01-01
Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…
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Designing a model for critical thinking development in AJA University of Medical Sciences
MAFAKHERI LALEH, MAHYAR; MOHAMMADIMEHR, MOJGAN; ZARGAR BALAYE JAME, SANAZ
2016-01-01
Introduction: In the new concept of medical education, creativity development is an important goal. The aim of this research was to identify a model for developing critical thinking among students with the special focus on learning environment and learning style. Methods: This applied and cross-sectional study was conducted among all students studying in undergraduate and professional doctorate programs in Fall Semester 2013-2014 in AJA University of Medical Sciences (N=777). The sample consisted of 257 students selected based on the proportional stratified random sampling method. To collect data, three questionnaires including Critical Thinking, Perception of Learning Environment and Learning Style were employed. The data were analyzed using Pearson's correlation statistical test, and one-sample t-test. The Structural Equation Model (SEM) was used to test the research model. SPSS software, version 14 and the LISREL software were used for data analysis. Results: The results showed that students had significantly assessed the teaching-learning environment and two components of "perception of teachers" and "perception of emotional-psychological climate" at the desirable level (pcritical thinking among students in terms of components of "commitment", "creativity" and "cognitive maturity" was at the relatively desirable level (pcritical thinking through learning style. Conclusion: One of the factors which can significantly impact the quality improvement of the teaching and learning process in AJA University of Medical Sciences is to develop critical thinking among learners. This issue requires providing the proper situation for teaching and learning critical thinking in the educational environment. PMID:27795968
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Enhanced surrogate models for statistical design exploiting space mapping technology
DEFF Research Database (Denmark)
Koziel, Slawek; Bandler, John W.; Mohamed, Achmed S.
2005-01-01
We present advances in microwave and RF device modeling exploiting Space Mapping (SM) technology. We propose new SM modeling formulations utilizing input mappings, output mappings, frequency scaling and quadratic approximations. Our aim is to enhance circuit models for statistical analysis...
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A new method to determine the number of experimental data using statistical modeling methods
Energy Technology Data Exchange (ETDEWEB)
Jung, Jung-Ho; Kang, Young-Jin; Lim, O-Kaung; Noh, Yoojeong [Pusan National University, Busan (Korea, Republic of)
2017-06-15
For analyzing the statistical performance of physical systems, statistical characteristics of physical parameters such as material properties need to be estimated by collecting experimental data. For accurate statistical modeling, many such experiments may be required, but data are usually quite limited owing to the cost and time constraints of experiments. In this study, a new method for determining a rea- sonable number of experimental data is proposed using an area metric, after obtaining statistical models using the information on the underlying distribution, the Sequential statistical modeling (SSM) approach, and the Kernel density estimation (KDE) approach. The area metric is used as a convergence criterion to determine the necessary and sufficient number of experimental data to be acquired. The pro- posed method is validated in simulations, using different statistical modeling methods, different true models, and different convergence criteria. An example data set with 29 data describing the fatigue strength coefficient of SAE 950X is used for demonstrating the performance of the obtained statistical models that use a pre-determined number of experimental data in predicting the probability of failure for a target fatigue life.
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Logarithmic transformed statistical models in calibration
International Nuclear Information System (INIS)
Zeis, C.D.
1975-01-01
A general type of statistical model used for calibration of instruments having the property that the standard deviations of the observed values increase as a function of the mean value is described. The application to the Helix Counter at the Rocky Flats Plant is primarily from a theoretical point of view. The Helix Counter measures the amount of plutonium in certain types of chemicals. The method described can be used also for other calibrations. (U.S.)
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Development of a statistical shape model of multi-organ and its performance evaluation
International Nuclear Information System (INIS)
Nakada, Misaki; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru
2010-01-01
Existing statistical shape modeling methods for an organ can not take into account the correlation between neighboring organs. This study focuses on a level set distribution model and proposes two modeling methods for multiple organs that can take into account the correlation between neighboring organs. The first method combines level set functions of multiple organs into a vector. Subsequently it analyses the distribution of the vectors of a training dataset by a principal component analysis and builds a multiple statistical shape model. Second method constructs a statistical shape model for each organ independently and assembles component scores of different organs in a training dataset so as to generate a vector. It analyses the distribution of the vectors of to build a statistical shape model of multiple organs. This paper shows results of applying the proposed methods trained by 15 abdominal CT volumes to unknown 8 CT volumes. (author)
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Statistical validation of normal tissue complication probability models
Xu, Cheng-Jian; van der Schaaf, Arjen; van t Veld, Aart; Langendijk, Johannes A.; Schilstra, Cornelis
2012-01-01
PURPOSE: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: A penalized regression method, LASSO (least absolute shrinkage
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International Nuclear Information System (INIS)
Lim, Gyeong Hui
2008-03-01
This book consists of 15 chapters, which are basic conception and meaning of statistical thermodynamics, Maxwell-Boltzmann's statistics, ensemble, thermodynamics function and fluctuation, statistical dynamics with independent particle system, ideal molecular system, chemical equilibrium and chemical reaction rate in ideal gas mixture, classical statistical thermodynamics, ideal lattice model, lattice statistics and nonideal lattice model, imperfect gas theory on liquid, theory on solution, statistical thermodynamics of interface, statistical thermodynamics of a high molecule system and quantum statistics
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Statistical models of petrol engines vehicles dynamics
Ilie, C. O.; Marinescu, M.; Alexa, O.; Vilău, R.; Grosu, D.
2017-10-01
This paper focuses on studying statistical models of vehicles dynamics. It was design and perform a one year testing program. There were used many same type cars with gasoline engines and different mileage. Experimental data were collected of onboard sensors and those on the engine test stand. A database containing data of 64th tests was created. Several mathematical modelling were developed using database and the system identification method. Each modelling is a SISO or a MISO linear predictive ARMAX (AutoRegressive-Moving-Average with eXogenous inputs) model. It represents a differential equation with constant coefficients. It were made 64th equations for each dependency like engine torque as output and engine’s load and intake manifold pressure, as inputs. There were obtained strings with 64 values for each type of model. The final models were obtained using average values of the coefficients. The accuracy of models was assessed.
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A Proposed Conceptual Model of Military Medical Readiness
National Research Council Canada - National Science Library
Van Hall, Brian M
2007-01-01
.... The purpose of this research is to consolidate existing literature on the latent variable of medical readiness, and to propose a composite theoretical model of medical readiness that may provide...
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Wu, Suo-Wei; Chen, Tong; Pan, Qi; Wei, Liang-Yu; Wang, Qin; Song, Jing-Chen; Li, Chao; Luo, Ji
2018-02-20
Cervical cancer shows a growing incidence and medical cost in recent years that has increased severe financial pressure on patients and medical insurance institutions. This study aimed to investigate the medical economic characteristics of cervical cancer patients with different payment modes within a Grade A tertiary hospital to provide evidence and suggestions for inpatient cost control and to verify the application of Gamma model in medical cost analysis. The basic and cost information of cervical cancer cases within a Grade A tertiary hospital in the year 2011-2016 were collected. The Gamma model was adopted to analyze the differences in each cost item between medical insured patient and uninsured patients. Meanwhile, the marginal means of different cost items were calculated to estimate the influence of payment modes toward different medical cost items among cervical cancer patients in the study. A total of 1321 inpatients with cervical cancer between the 2011 and 2016 were collected through the medical records system. Of the 1321 cases, 65.9% accounted for medical insured patients and 34.1% were uninsured patients. The total inpatient medical expenditure of insured patients was RMB 29,509.1 Yuan and uninsured patients was RMB 22,114.3 Yuan, respectively. Payment modes, therapeutic options as well as the recurrence and metastasis of tumor toward the inpatient medical expenditures between the two groups were statistically significant. To the specifics, drug costs accounted for 37.7% and 33.8% of the total, surgery costs accounted for 21.5% and 25.5%, treatment costs accounted for 18.7% and 16.4%, whereas the costs of imaging and laboratory examinations accounted for 16.4% and 15.2% for the insured patient and uninsured patients, respectively. As the effects of covariates were controlled, the total hospitalization costs, drug costs, treatment costs as well as imaging and laboratory examination costs showed statistical significance. The total hospitalization
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International Nuclear Information System (INIS)
Saito, Toki; Nakajima, Yoshikazu; Sugita, Naohiko; Mitsuishi, Mamoru; Hashizume, Hiroyuki; Kuramoto, Kouichi; Nakashima, Yosio
2011-01-01
Statistical deformable model based two-dimensional/three-dimensional (2-D/3-D) registration is a promising method for estimating the position and shape of patient bone in the surgical space. Since its accuracy depends on the statistical model capacity, we propose a method for accurately generating a statistical bone model from a CT volume. Our method employs the Sphere-Attribute-Image (SAI) and has improved the accuracy of corresponding point search in statistical model generation. At first, target bone surfaces are extracted as SAIs from the CT volume. Then the textures of SAIs are classified to some regions using Maximally-stable-extremal-regions methods. Next, corresponding regions are determined using Normalized cross-correlation (NCC). Finally, corresponding points in each corresponding region are determined using NCC. The application of our method to femur bone models was performed, and worked well in the experiments. (author)
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Shape analysis in medical image analysis
Tavares, João
2014-01-01
This book contains thirteen contributions from invited experts of international recognition addressing important issues in shape analysis in medical image analysis, including techniques for image segmentation, registration, modelling and classification, and applications in biology, as well as in cardiac, brain, spine, chest, lung and clinical practice. This volume treats topics such as, anatomic and functional shape representation and matching; shape-based medical image segmentation; shape registration; statistical shape analysis; shape deformation; shape-based abnormity detection; shape tracking and longitudinal shape analysis; machine learning for shape modeling and analysis; shape-based computer-aided-diagnosis; shape-based medical navigation; benchmark and validation of shape representation, analysis and modeling algorithms. This work will be of interest to researchers, students, and manufacturers in the fields of artificial intelligence, bioengineering, biomechanics, computational mechanics, computationa...
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Statistical modeling of static strengths of nuclear graphites with relevance to structural design
International Nuclear Information System (INIS)
Arai, Taketoshi
1992-02-01
Use of graphite materials for structural members poses a problem as to how to take into account of statistical properties of static strength, especially tensile fracture stresses, in component structural design. The present study concerns comprehensive examinations on statistical data base and modelings on nuclear graphites. First, the report provides individual samples and their analyses on strengths of IG-110 and PGX graphites for HTTR components. Those statistical characteristics on other HTGR graphites are also exemplified from the literature. Most of statistical distributions of individual samples are found to be approximately normal. The goodness of fit to normal distributions is more satisfactory with larger sample sizes. Molded and extruded graphites, however, possess a variety of statistical properties depending of samples from different with-in-log locations and/or different orientations. Second, the previous statistical models including the Weibull theory are assessed from the viewpoint of applicability to design procedures. This leads to a conclusion that the Weibull theory and its modified ones are satisfactory only for limited parts of tensile fracture behavior. They are not consistent for whole observations. Only normal statistics are justifiable as practical approaches to discuss specified minimum ultimate strengths as statistical confidence limits for individual samples. Third, the assessment of various statistical models emphasizes the need to develop advanced analytical ones which should involve modeling of microstructural features of actual graphite materials. Improvements of other structural design methodologies are also presented. (author)
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International Nuclear Information System (INIS)
Lovejoy, S.; Lima, M. I. P. de
2015-01-01
Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time
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De Leeuw, R A; Westerman, Michiel; Nelson, E; Ket, J C F; Scheele, F
2016-07-08
E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings.
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Monitor-Based Statistical Model Checking for Weighted Metric Temporal Logic
DEFF Research Database (Denmark)
Bulychev, Petr; David, Alexandre; Larsen, Kim Guldstrand
2012-01-01
We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction with desi......We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction...
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Statistical Model of the 2001 Czech Census for Interactive Presentation
Czech Academy of Sciences Publication Activity Database
Grim, Jiří; Hora, Jan; Boček, Pavel; Somol, Petr; Pudil, Pavel
Vol. 26, č. 4 (2010), s. 1-23 ISSN 0282-423X R&D Projects: GA ČR GA102/07/1594; GA MŠk 1M0572 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Interactive statistical model * census data presentation * distribution mixtures * data modeling * EM algorithm * incomplete data * data reproduction accuracy * data mining Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.492, year: 2010 http://library.utia.cas.cz/separaty/2010/RO/grim-0350513.pdf
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Dual processing model of medical decision-making
Directory of Open Access Journals (Sweden)
Djulbegovic Benjamin
2012-09-01
Full Text Available Abstract Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I and/or an analytical, deliberative (system II processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to
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Chen, Jinsong; Liu, Lei; Shih, Ya-Chen T; Zhang, Daowen; Severini, Thomas A
2016-03-15
We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B-splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method. Copyright © 2015 John Wiley & Sons, Ltd.
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A statistical mechanical model of economics
Lubbers, Nicholas Edward Williams
Statistical mechanics pursues low-dimensional descriptions of systems with a very large number of degrees of freedom. I explore this theme in two contexts. The main body of this dissertation explores and extends the Yard Sale Model (YSM) of economic transactions using a combination of simulations and theory. The YSM is a simple interacting model for wealth distributions which has the potential to explain the empirical observation of Pareto distributions of wealth. I develop the link between wealth condensation and the breakdown of ergodicity due to nonlinear diffusion effects which are analogous to the geometric random walk. Using this, I develop a deterministic effective theory of wealth transfer in the YSM that is useful for explaining many quantitative results. I introduce various forms of growth to the model, paying attention to the effect of growth on wealth condensation, inequality, and ergodicity. Arithmetic growth is found to partially break condensation, and geometric growth is found to completely break condensation. Further generalizations of geometric growth with growth in- equality show that the system is divided into two phases by a tipping point in the inequality parameter. The tipping point marks the line between systems which are ergodic and systems which exhibit wealth condensation. I explore generalizations of the YSM transaction scheme to arbitrary betting functions to develop notions of universality in YSM-like models. I find that wealth vi condensation is universal to a large class of models which can be divided into two phases. The first exhibits slow, power-law condensation dynamics, and the second exhibits fast, finite-time condensation dynamics. I find that the YSM, which exhibits exponential dynamics, is the critical, self-similar model which marks the dividing line between the two phases. The final chapter develops a low-dimensional approach to materials microstructure quantification. Modern materials design harnesses complex
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Statistically Modeling I-V Characteristics of CNT-FET with LASSO
Ma, Dongsheng; Ye, Zuochang; Wang, Yan
2017-08-01
With the advent of internet of things (IOT), the need for studying new material and devices for various applications is increasing. Traditionally we build compact models for transistors on the basis of physics. But physical models are expensive and need a very long time to adjust for non-ideal effects. As the vision for the application of many novel devices is not certain or the manufacture process is not mature, deriving generalized accurate physical models for such devices is very strenuous, whereas statistical modeling is becoming a potential method because of its data oriented property and fast implementation. In this paper, one classical statistical regression method, LASSO, is used to model the I-V characteristics of CNT-FET and a pseudo-PMOS inverter simulation based on the trained model is implemented in Cadence. The normalized relative mean square prediction error of the trained model versus experiment sample data and the simulation results show that the model is acceptable for digital circuit static simulation. And such modeling methodology can extend to general devices.
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Semantic concept-enriched dependence model for medical information retrieval.
Choi, Sungbin; Choi, Jinwook; Yoo, Sooyoung; Kim, Heechun; Lee, Youngho
2014-02-01
In medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a semantic concept-based term-dependence feature into a formal retrieval model to improve its ranking performance. Standardized medical concept terms used by medical professionals were assumed to have implicit dependency within the same concept. We hypothesized that, by elaborately revising the ranking algorithms to favor documents that preserve those implicit dependencies, the ranking performance could be improved. The implicit dependence features are harvested from the original query using MetaMap. These semantic concept-based dependence features were incorporated into a semantic concept-enriched dependence model (SCDM). We designed four different variants of the model, with each variant having distinct characteristics in the feature formulation method. We performed leave-one-out cross validations on both a clinical document corpus (TREC Medical records track) and a medical literature corpus (OHSUMED), which are representative test collections in medical information retrieval research. Our semantic concept-enriched dependence model consistently outperformed other state-of-the-art retrieval methods. Analysis shows that the performance gain has occurred independently of the concept's explicit importance in the query. By capturing implicit knowledge with regard to the query term relationships and incorporating them into a ranking model, we could build a more robust and effective retrieval model, independent of the concept importance. Copyright © 2013 Elsevier Inc. All rights reserved.
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MacKenzie, Dana
2004-01-01
The drawbacks of using 19th-century mathematics in physics and astronomy are illustrated. To continue with the expansion of the knowledge about the cosmos, the scientists will have to come in terms with modern statistics. Some researchers have deliberately started importing techniques that are used in medical research. However, the physicists need to identify the brand of statistics that will be suitable for them, and make a choice between the Bayesian and the frequentists approach. (Edited abstract).
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Nick, Todd G
2007-01-01
Statistics is defined by the Medical Subject Headings (MeSH) thesaurus as the science and art of collecting, summarizing, and analyzing data that are subject to random variation. The two broad categories of summarizing and analyzing data are referred to as descriptive and inferential statistics. This chapter considers the science and art of summarizing data where descriptive statistics and graphics are used to display data. In this chapter, we discuss the fundamentals of descriptive statistics, including describing qualitative and quantitative variables. For describing quantitative variables, measures of location and spread, for example the standard deviation, are presented along with graphical presentations. We also discuss distributions of statistics, for example the variance, as well as the use of transformations. The concepts in this chapter are useful for uncovering patterns within the data and for effectively presenting the results of a project.
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Sapsis, Themistoklis P; Majda, Andrew J
2013-08-20
A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.
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Statistics of a neuron model driven by asymmetric colored noise.
Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin
2015-02-01
Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.
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Highly Robust Statistical Methods in Medical Image Analysis
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2012-01-01
Roč. 32, č. 2 (2012), s. 3-16 ISSN 0208-5216 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust statistics * classification * faces * robust image analysis * forensic science Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.208, year: 2012 http://www.ibib.waw.pl/bbe/bbefulltext/BBE_32_2_003_FT.pdf
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Statistical aspects of carbon fiber risk assessment modeling. [fire accidents involving aircraft
Gross, D.; Miller, D. R.; Soland, R. M.
1980-01-01
The probabilistic and statistical aspects of the carbon fiber risk assessment modeling of fire accidents involving commercial aircraft are examined. Three major sources of uncertainty in the modeling effort are identified. These are: (1) imprecise knowledge in establishing the model; (2) parameter estimation; and (3)Monte Carlo sampling error. All three sources of uncertainty are treated and statistical procedures are utilized and/or developed to control them wherever possible.
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Model output statistics applied to wind power prediction
Energy Technology Data Exchange (ETDEWEB)
Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)
1999-03-01
Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.
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Modeling of Dissipation Element Statistics in Turbulent Non-Premixed Jet Flames
Denker, Dominik; Attili, Antonio; Boschung, Jonas; Hennig, Fabian; Pitsch, Heinz
2017-11-01
The dissipation element (DE) analysis is a method for analyzing and compartmentalizing turbulent scalar fields. DEs can be described by two parameters, namely the Euclidean distance l between their extremal points and the scalar difference in the respective points Δϕ . The joint probability density function (jPDF) of these two parameters P(Δϕ , l) is expected to suffice for a statistical reconstruction of the scalar field. In addition, reacting scalars show a strong correlation with these DE parameters in both premixed and non-premixed flames. Normalized DE statistics show a remarkable invariance towards changes in Reynolds numbers. This feature of DE statistics was exploited in a Boltzmann-type evolution equation based model for the probability density function (PDF) of the distance between the extremal points P(l) in isotropic turbulence. Later, this model was extended for the jPDF P(Δϕ , l) and then adapted for the use in free shear flows. The effect of heat release on the scalar scales and DE statistics is investigated and an extended model for non-premixed jet flames is introduced, which accounts for the presence of chemical reactions. This new model is validated against a series of DNS of temporally evolving jet flames. European Research Council Project ``Milestone''.
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Model-based engineering for medical-device software.
Ray, Arnab; Jetley, Raoul; Jones, Paul L; Zhang, Yi
2010-01-01
This paper demonstrates the benefits of adopting model-based design techniques for engineering medical device software. By using a patient-controlled analgesic (PCA) infusion pump as a candidate medical device, the authors show how using models to capture design information allows for i) fast and efficient construction of executable device prototypes ii) creation of a standard, reusable baseline software architecture for a particular device family, iii) formal verification of the design against safety requirements, and iv) creation of a safety framework that reduces verification costs for future versions of the device software. 1.
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Statistical Model Calculations for (n,γ Reactions
Directory of Open Access Journals (Sweden)
Beard Mary
2015-01-01
Full Text Available Hauser-Feshbach (HF cross sections are of enormous importance for a wide range of applications, from waste transmutation and nuclear technologies, to medical applications, and nuclear astrophysics. It is a well-observed result that different nuclear input models sensitively affect HF cross section calculations. Less well known however are the effects on calculations originating from model-specific implementation details (such as level density parameter, matching energy, back-shift and giant dipole parameters, as well as effects from non-model aspects, such as experimental data truncation and transmission function energy binning. To investigate the effects or these various aspects, Maxwellian-averaged neutron capture cross sections have been calculated for approximately 340 nuclei. The relative effects of these model details will be discussed.
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Dialect topic modeling for improved consumer medical search.
Crain, Steven P; Yang, Shuang-Hong; Zha, Hongyuan; Jiao, Yu
2010-11-13
Access to health information by consumers is hampered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, common and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations using consumer questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.
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Dialect Topic Modeling for Improved Consumer Medical Search
Energy Technology Data Exchange (ETDEWEB)
Crain, Steven P. [Georgia Institute of Technology; Yang, Shuang-Hong [Georgia Institute of Technology; Zha, Hongyuan [Georgia Institute of Technology; Jiao, Yu [ORNL
2010-01-01
Access to health information by consumers is ham- pered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, com- mon and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations us- ing consumer questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.
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Model selection for contingency tables with algebraic statistics
Krampe, A.; Kuhnt, S.; Gibilisco, P.; Riccimagno, E.; Rogantin, M.P.; Wynn, H.P.
2009-01-01
Goodness-of-fit tests based on chi-square approximations are commonly used in the analysis of contingency tables. Results from algebraic statistics combined with MCMC methods provide alternatives to the chi-square approximation. However, within a model selection procedure usually a large number of
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New robust statistical procedures for the polytomous logistic regression models.
Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro
2018-05-17
This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.
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Relevance of the c-statistic when evaluating risk-adjustment models in surgery.
Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y
2012-05-01
The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become
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Probing the exchange statistics of one-dimensional anyon models
Greschner, Sebastian; Cardarelli, Lorenzo; Santos, Luis
2018-05-01
We propose feasible scenarios for revealing the modified exchange statistics in one-dimensional anyon models in optical lattices based on an extension of the multicolor lattice-depth modulation scheme introduced in [Phys. Rev. A 94, 023615 (2016), 10.1103/PhysRevA.94.023615]. We show that the fast modulation of a two-component fermionic lattice gas in the presence a magnetic field gradient, in combination with additional resonant microwave fields, allows for the quantum simulation of hardcore anyon models with periodic boundary conditions. Such a semisynthetic ring setup allows for realizing an interferometric arrangement sensitive to the anyonic statistics. Moreover, we show as well that simple expansion experiments may reveal the formation of anomalously bound pairs resulting from the anyonic exchange.
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Bayesian Sensitivity Analysis of Statistical Models with Missing Data.
Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng
2014-04-01
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.
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Atmospheric corrosion: statistical validation of models
International Nuclear Information System (INIS)
Diaz, V.; Martinez-Luaces, V.; Guineo-Cobs, G.
2003-01-01
In this paper we discuss two different methods for validation of regression models, applied to corrosion data. One of them is based on the correlation coefficient and the other one is the statistical test of lack of fit. Both methods are used here to analyse fitting of bi logarithmic model in order to predict corrosion for very low carbon steel substrates in rural and urban-industrial atmospheres in Uruguay. Results for parameters A and n of the bi logarithmic model are reported here. For this purpose, all repeated values were used instead of using average values as usual. Modelling is carried out using experimental data corresponding to steel substrates under the same initial meteorological conditions ( in fact, they are put in the rack at the same time). Results of correlation coefficient are compared with the lack of it tested at two different signification levels (α=0.01 and α=0.05). Unexpected differences between them are explained and finally, it is possible to conclude, at least in the studied atmospheres, that the bi logarithmic model does not fit properly the experimental data. (Author) 18 refs
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Automatic generation of statistical pose and shape models for articulated joints.
Xin Chen; Graham, Jim; Hutchinson, Charles; Muir, Lindsay
2014-02-01
Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.
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Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T
2016-05-01
Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.
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What type of statistical model to choose for the analysis of radioimmunoassays
International Nuclear Information System (INIS)
Huet, S.
1984-01-01
The current techniques used for statistical analysis of radioimmunoassays are not very satisfactory for either the statistician or the biologist. They are based on an attempt to make the response curve linear to avoid complicated computations. The present article shows that this practice has considerable effects (often neglected) on the statistical assumptions which must be formulated. A more strict analysis is proposed by applying the four-parameter logistic model. The advantages of this method are: the statistical assumptions formulated are based on observed data, and the model can be applied to almost all radioimmunoassays [fr
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Multimesonic decays of charmonium states in the statistical quark model
International Nuclear Information System (INIS)
Montvay, I.; Toth, J.D.
1978-01-01
The data known at present of multimesonic decays of chi and psi states are fitted in a statistical quark model, in which the matrix elements are assumed to be constant and resonances as well as both strong and second order electromagnetic processes are taken into account. The experimental data are well reproduced by the model. Unknown branching ratios for the rest of multimesonic channels are predicted. The fit leaves about 40% for baryonic and radiative channels in the case of J/psi(3095). The fitted parameters of the J/psi decays are used to predict the mesonic decays of the pseudoscalar eta c. The statistical quark model seems to allow the calculation of competitive multiparticle processes for the studied decays. (D.P.)
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Uniting statistical and individual-based approaches for animal movement modelling.
Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel
2014-01-01
The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.
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Statistical inference to advance network models in epidemiology.
Welch, David; Bansal, Shweta; Hunter, David R
2011-03-01
Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data. Copyright © 2011 Elsevier B.V. All rights reserved.
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Directory of Open Access Journals (Sweden)
Annisa N. Insany
2015-06-01
Full Text Available High prevalence of self medication with antibiotics can increase the probability of irrational use of antibiotics which may lead antibiotics resistance. Thus, shifting of behavior is required to minimize the irrational use of antibiotics. This study was aimed to determine the association between public perceived value and self-medication with antibiotics which can be used to develop an intervention model in order to reduce the practice of self-medication with antibiotics. An observational study was conducted during the period of November–December 2014.The subjects were patients who visit primary health care facilities in Bandung. A structured-interview that has been validated was used to investigate the association between perceived value and self-medication behavior based on the Health Belief Model theory (perceived susceptibility, benefits, barrier, and cues to action. Approximately 506 respondents were drawn randomly from 43 community healthcare centers and 8 pharmacies. Data was analyzed by using descriptive statistics and logistic regression (CI 95%, α = 5%. Validity and reliability of the questionnaire were shown with a correlation coefficient of >0.3 and a cronbach-alpha value of 0.719, respectively. We found that 29.45% of respondents practiced self-medication with antibiotics over the last six months. Additionally, there was no significant association between the perceived susceptibility, benefits, barrier, and cues to action with self-medication behavior (p>0.05. Easiness to access antibiotics without prescription was presumed as a factor that contribute to self-medication with antibiotics, therefore strict regulation in antibiotics use is very needed as a basic intervention to decrease self-medication with antibiotic.
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Statistical properties of several models of fractional random point processes
Bendjaballah, C.
2011-08-01
Statistical properties of several models of fractional random point processes have been analyzed from the counting and time interval statistics points of view. Based on the criterion of the reduced variance, it is seen that such processes exhibit nonclassical properties. The conditions for these processes to be treated as conditional Poisson processes are examined. Numerical simulations illustrate part of the theoretical calculations.
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Medical Writing Competency Model - Section 1: Functions, Tasks, and Activities.
Clemow, David B; Wagner, Bertil; Marshallsay, Christopher; Benau, Dan; L'Heureux, Darryl; Brown, David H; Dasgupta, Devjani Ghosh; Girten, Eileen; Hubbard, Frank; Gawrylewski, Helle-Mai; Ebina, Hiroko; Stoltenborg, Janet; York, J P; Green, Kim; Wood, Linda Fossati; Toth, Lisa; Mihm, Michael; Katz, Nancy R; Vasconcelos, Nina-Maria; Sakiyama, Norihisa; Whitsell, Robin; Gopalakrishnan, Shobha; Bairnsfather, Susan; Wanderer, Tatyana; Schindler, Thomas M; Mikyas, Yeshi; Aoyama, Yumiko
2018-01-01
This article provides Section 1 of the 2017 Edition 2 Medical Writing Competency Model that describes the core work functions and associated tasks and activities related to professional medical writing within the life sciences industry. The functions in the Model are scientific communication strategy; document preparation, development, and finalization; document project management; document template, standard, format, and style development and maintenance; outsourcing, alliance partner, and client management; knowledge, skill, ability, and behavior development and sharing; and process improvement. The full Model also includes Section 2, which covers the knowledge, skills, abilities, and behaviors needed for medical writers to be effective in their roles; Section 2 is presented in a companion article. Regulatory, publication, and other scientific writing as well as management of writing activities are covered. The Model was developed to aid medical writers and managers within the life sciences industry regarding medical writing hiring, training, expectation and goal setting, performance evaluation, career development, retention, and role value sharing to cross-functional partners.
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2011 Aerospace Medical Certification Statistical Handbook
2013-01-01
from the Aviation Medical Examiner Information System (AMEIS). The current status of each AME was determined for each year of the study period from 2009...current status of each AME was determined for each year of the study period from 2009-2011, retaining only those with an active status. Airman...gastroplasty, dysphagia , dyspepsia, thrombus abdomen aorta# 22,187 3.73 Other skin conditions – includes acne, abnormal pigmentation, vitiligo
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Statistical Power Analysis with Missing Data A Structural Equation Modeling Approach
Davey, Adam
2009-01-01
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types
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The conceptual basis of mathematics in cardiology IV: statistics and model fitting.
Bates, Jason H T; Sobel, Burton E
2003-06-01
This is the fourth in a series of four articles developed for the readers of Coronary Artery Disease. Without language ideas cannot be articulated. What may not be so immediately obvious is that they cannot be formulated either. One of the essential languages of cardiology is mathematics. Unfortunately, medical education does not emphasize, and in fact, often neglects empowering physicians to think mathematically. Reference to statistics, conditional probability, multicompartmental modeling, algebra, calculus and transforms is common but often without provision of genuine conceptual understanding. At the University of Vermont College of Medicine, Professor Bates developed a course designed to address these deficiencies. The course covered mathematical principles pertinent to clinical cardiovascular and pulmonary medicine and research. It focused on fundamental concepts to facilitate formulation and grasp of ideas. This series of four articles was developed to make the material available for a wider audience. The articles will be published sequentially in Coronary Artery Disease. Beginning with fundamental axioms and basic algebraic manipulations they address algebra, function and graph theory, real and complex numbers, calculus and differential equations, mathematical modeling, linear system theory and integral transforms and statistical theory. The principles and concepts they address provide the foundation needed for in-depth study of any of these topics. Perhaps of even more importance, they should empower cardiologists and cardiovascular researchers to utilize the language of mathematics in assessing the phenomena of immediate pertinence to diagnosis, pathophysiology and therapeutics. The presentations are interposed with queries (by Coronary Artery Disease abbreviated as CAD) simulating the nature of interactions that occurred during the course itself. Each article concludes with one or more examples illustrating application of the concepts covered to
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Statistical methods for mechanistic model validation: Salt Repository Project
International Nuclear Information System (INIS)
Eggett, D.L.
1988-07-01
As part of the Department of Energy's Salt Repository Program, Pacific Northwest Laboratory (PNL) is studying the emplacement of nuclear waste containers in a salt repository. One objective of the SRP program is to develop an overall waste package component model which adequately describes such phenomena as container corrosion, waste form leaching, spent fuel degradation, etc., which are possible in the salt repository environment. The form of this model will be proposed, based on scientific principles and relevant salt repository conditions with supporting data. The model will be used to predict the future characteristics of the near field environment. This involves several different submodels such as the amount of time it takes a brine solution to contact a canister in the repository, how long it takes a canister to corrode and expose its contents to the brine, the leach rate of the contents of the canister, etc. These submodels are often tested in a laboratory and should be statistically validated (in this context, validate means to demonstrate that the model adequately describes the data) before they can be incorporated into the waste package component model. This report describes statistical methods for validating these models. 13 refs., 1 fig., 3 tabs
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A smart medication recommendation model for the electronic prescription.
Syed-Abdul, Shabbir; Nguyen, Alex; Huang, Frank; Jian, Wen-Shan; Iqbal, Usman; Yang, Vivian; Hsu, Min-Huei; Li, Yu-Chuan
2014-11-01
The report from the Institute of Medicine, To Err Is Human: Building a Safer Health System in 1999 drew a special attention towards preventable medical errors and patient safety. The American Reinvestment and Recovery Act of 2009 and federal criteria of 'Meaningful use' stage 1 mandated e-prescribing to be used by eligible providers in order to access Medicaid and Medicare incentive payments. Inappropriate prescribing has been identified as a preventable cause of at least 20% of drug-related adverse events. A few studies reported system-related errors and have offered targeted recommendations on improving and enhancing e-prescribing system. This study aims to enhance efficiency of the e-prescribing system by shortening the medication list, reducing the risk of inappropriate selection of medication, as well as in reducing the prescribing time of physicians. 103.48 million prescriptions from Taiwan's national health insurance claim data were used to compute Diagnosis-Medication association. Furthermore, 100,000 prescriptions were randomly selected to develop a smart medication recommendation model by using association rules of data mining. The important contribution of this model is to introduce a new concept called Mean Prescription Rank (MPR) of prescriptions and Coverage Rate (CR) of prescriptions. A proactive medication list (PML) was computed using MPR and CR. With this model the medication drop-down menu is significantly shortened, thereby reducing medication selection errors and prescription times. The physicians will still select relevant medications even in the case of inappropriate (unintentional) selection. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Statistical Modeling of Energy Production by Photovoltaic Farms
Czech Academy of Sciences Publication Activity Database
Brabec, Marek; Pelikán, Emil; Krč, Pavel; Eben, Kryštof; Musílek, P.
2011-01-01
Roč. 5, č. 9 (2011), s. 785-793 ISSN 1934-8975 Grant - others:GA AV ČR(CZ) M100300904 Institutional research plan: CEZ:AV0Z10300504 Keywords : electrical energy * solar energy * numerical weather prediction model * nonparametric regression * beta regression Subject RIV: BB - Applied Statistics, Operational Research
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Profile of Research Methodology and Statistics Training of ...
African Journals Online (AJOL)
Background: Medical practitioners need to have knowledge of statistics and research principles, especially with the increasing emphasis on evidence-based medicine. The aim of this study was to determine the profile of research methodology and statistics training of undergraduate medical students at South African ...
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Statistical analysis of probabilistic models of software product lines with quantitative constraints
DEFF Research Database (Denmark)
Beek, M.H. ter; Legay, A.; Lluch Lafuente, Alberto
2015-01-01
We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra...... of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study....
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International Nuclear Information System (INIS)
Weathers, J.B.; Luck, R.; Weathers, J.W.
2009-01-01
The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.
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Energy Technology Data Exchange (ETDEWEB)
Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com
2009-11-15
The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.
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International Nuclear Information System (INIS)
Potter, G.L.; Ellsaesser, H.W.; MacCracken, M.C.; Luther, F.M.
1978-06-01
Results from the zonal model indicate quite reasonable agreement with observation in terms of the parameters and processes that influence the radiation and energy balance calculations. The model produces zonal statistics similar to those from general circulation models, and has also been shown to produce similar responses in sensitivity studies. Further studies of model performance are planned, including: comparison with July data; comparison of temperature and moisture transport and wind fields for winter and summer months; and a tabulation of atmospheric energetics. Based on these preliminary performance studies, however, it appears that the zonal model can be used in conjunction with more complex models to help unravel the problems of understanding the processes governing present climate and climate change. As can be seen in the subsequent paper on model sensitivity studies, in addition to reduced cost of computation, the zonal model facilitates analysis of feedback mechanisms and simplifies analysis of the interactions between processes
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Statistical shear lag model - unraveling the size effect in hierarchical composites.
Wei, Xiaoding; Filleter, Tobin; Espinosa, Horacio D
2015-05-01
Numerous experimental and computational studies have established that the hierarchical structures encountered in natural materials, such as the brick-and-mortar structure observed in sea shells, are essential for achieving defect tolerance. Due to this hierarchy, the mechanical properties of natural materials have a different size dependence compared to that of typical engineered materials. This study aimed to explore size effects on the strength of bio-inspired staggered hierarchical composites and to define the influence of the geometry of constituents in their outstanding defect tolerance capability. A statistical shear lag model is derived by extending the classical shear lag model to account for the statistics of the constituents' strength. A general solution emerges from rigorous mathematical derivations, unifying the various empirical formulations for the fundamental link length used in previous statistical models. The model shows that the staggered arrangement of constituents grants composites a unique size effect on mechanical strength in contrast to homogenous continuous materials. The model is applied to hierarchical yarns consisting of double-walled carbon nanotube bundles to assess its predictive capabilities for novel synthetic materials. Interestingly, the model predicts that yarn gauge length does not significantly influence the yarn strength, in close agreement with experimental observations. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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Modelling diversity in building occupant behaviour: a novel statistical approach
DEFF Research Database (Denmark)
Haldi, Frédéric; Calì, Davide; Andersen, Rune Korsholm
2016-01-01
We propose an advanced modelling framework to predict the scope and effects of behavioural diversity regarding building occupant actions on window openings, shading devices and lighting. We develop a statistical approach based on generalised linear mixed models to account for the longitudinal nat...
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Manning, Robert M.
1990-01-01
A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.
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Effect of model choice and sample size on statistical tolerance limits
International Nuclear Information System (INIS)
Duran, B.S.; Campbell, K.
1980-03-01
Statistical tolerance limits are estimates of large (or small) quantiles of a distribution, quantities which are very sensitive to the shape of the tail of the distribution. The exact nature of this tail behavior cannot be ascertained brom small samples, so statistical tolerance limits are frequently computed using a statistical model chosen on the basis of theoretical considerations or prior experience with similar populations. This report illustrates the effects of such choices on the computations
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Model medication management process in Australian nursing homes using business process modeling.
Qian, Siyu; Yu, Ping
2013-01-01
One of the reasons for end user avoidance or rejection to use health information systems is poor alignment of the system with healthcare workflow, likely causing by system designers' lack of thorough understanding about healthcare process. Therefore, understanding the healthcare workflow is the essential first step for the design of optimal technologies that will enable care staff to complete the intended tasks faster and better. The often use of multiple or "high risk" medicines by older people in nursing homes has the potential to increase medication error rate. To facilitate the design of information systems with most potential to improve patient safety, this study aims to understand medication management process in nursing homes using business process modeling method. The paper presents study design and preliminary findings from interviewing two registered nurses, who were team leaders in two nursing homes. Although there were subtle differences in medication management between the two homes, major medication management activities were similar. Further field observation will be conducted. Based on the data collected from observations, an as-is process model for medication management will be developed.
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Statistical properties of the nuclear shell-model Hamiltonian
International Nuclear Information System (INIS)
Dias, H.; Hussein, M.S.; Oliveira, N.A. de
1986-01-01
The statistical properties of realistic nuclear shell-model Hamiltonian are investigated in sd-shell nuclei. The probability distribution of the basic-vector amplitude is calculated and compared with the Porter-Thomas distribution. Relevance of the results to the calculation of the giant resonance mixing parameter is pointed out. (Author) [pt
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On the statistical comparison of climate model output and climate data
International Nuclear Information System (INIS)
Solow, A.R.
1991-01-01
Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question. 9 refs
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On the statistical comparison of climate model output and climate data
International Nuclear Information System (INIS)
Solow, A.R.
1990-01-01
Some broad issues arising in the statistical comparison of the output of climate models with the corresponding climate data are reviewed. Particular attention is paid to the question of detecting climate change. The purpose of this paper is to review some statistical approaches to the comparison of the output of climate models with climate data. There are many statistical issues arising in such a comparison. The author will focus on some of the broader issues, although some specific methodological questions will arise along the way. One important potential application of the approaches discussed in this paper is the detection of climate change. Although much of the discussion will be fairly general, he will try to point out the appropriate connections to the detection question
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Statistical Models to Assess the Health Effects and to Forecast Ground Level Ozone
Czech Academy of Sciences Publication Activity Database
Schlink, U.; Herbath, O.; Richter, M.; Dorling, S.; Nunnari, G.; Cawley, G.; Pelikán, Emil
2006-01-01
Roč. 21, č. 4 (2006), s. 547-558 ISSN 1364-8152 R&D Projects: GA AV ČR 1ET400300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : statistical models * ground level ozone * health effects * logistic model * forecasting * prediction performance * neural network * generalised additive model * integrated assessment Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.992, year: 2006
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Müller, M. F.; Thompson, S. E.
2015-09-01
The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.
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On an uncorrelated jet model with Bose-Einstein statistics
International Nuclear Information System (INIS)
Bilic, N.; Dadic, I.; Martinis, M.
1978-01-01
Starting from the density of states of an ideal Bose-Einstein gas, an uncorrelated jet model with Bose-Einstein statistics has been formulated. The transition to continuum is based on the Touschek invariant measure. It has been shown that in this model average multiplicity increases logarithmically with total energy, while the inclusive distribution shows ln s violation of scaling. (author)
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Medical Professionals Designing Hospital Management Models
DEFF Research Database (Denmark)
Byg, Vibeke
Health care administration in many OECD countries has undergone substantial changes in recent years as a consequence of NPM reforms, rising costs, the pace of technological innovation, heightened competition for patients and resources, quality of managed care and demographic shifts. Hospitals...... especially have been reformed due to the high proportion of resources they absorb and the apparent difficulty of prioritizing and coordinating health care within hospitals. There is abundant research literature on the topic of reforming hospital management models. Lacking from the literature, however......, is insight into how we can understand and explain how medical professionals adapt hospital management over time in relation to changing hospital management models that are global in their influence in hospital organizations. The aim of this dissertation is to understand and explain how medical professionals...
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Paprotny, D.; Morales Napoles, O.; Jonkman, S.N.
2017-01-01
Flood hazard is currently being researched on continental and global scales, using models of increasing complexity. In this paper we investigate a different, simplified approach, which combines statistical and physical models in place of conventional rainfall-run-off models to carry out flood
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A simple statistical model for geomagnetic reversals
Constable, Catherine
1990-01-01
The diversity of paleomagnetic records of geomagnetic reversals now available indicate that the field configuration during transitions cannot be adequately described by simple zonal or standing field models. A new model described here is based on statistical properties inferred from the present field and is capable of simulating field transitions like those observed. Some insight is obtained into what one can hope to learn from paleomagnetic records. In particular, it is crucial that the effects of smoothing in the remanence acquisition process be separated from true geomagnetic field behavior. This might enable us to determine the time constants associated with the dominant field configuration during a reversal.
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Vortex dynamics and Lagrangian statistics in a model for active turbulence.
James, Martin; Wilczek, Michael
2018-02-14
Cellular suspensions such as dense bacterial flows exhibit a turbulence-like phase under certain conditions. We study this phenomenon of "active turbulence" statistically by using numerical tools. Following Wensink et al. (Proc. Natl. Acad. Sci. U.S.A. 109, 14308 (2012)), we model active turbulence by means of a generalized Navier-Stokes equation. Two-point velocity statistics of active turbulence, both in the Eulerian and the Lagrangian frame, is explored. We characterize the scale-dependent features of two-point statistics in this system. Furthermore, we extend this statistical study with measurements of vortex dynamics in this system. Our observations suggest that the large-scale statistics of active turbulence is close to Gaussian with sub-Gaussian tails.
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Thiessen, Erik D
2017-01-05
Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik
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Directory of Open Access Journals (Sweden)
De las Cuevas C
2017-03-01
positively (although weakly with age. Self-efficacy indirectly influenced treatment adherence through internal health locus of control.Conclusion: This study provides support for the hypothesis that perceived health control variables play a relevant role in psychiatric patients’ adherence to psychopharmacological medications. The findings highlight the importance of considering prospective studies of patients’ psychological reactance and health locus of control as they may be clinically relevant factors contributing to adherence to psychopharmacological medications. Keywords: attitude to health, medication adherence, health behavior, internal-external control, model, statistical, psychiatry
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Discrete ellipsoidal statistical BGK model and Burnett equations
Zhang, Yu-Dong; Xu, Ai-Guo; Zhang, Guang-Cai; Chen, Zhi-Hua; Wang, Pei
2018-06-01
A new discrete Boltzmann model, the discrete ellipsoidal statistical Bhatnagar-Gross-Krook (ESBGK) model, is proposed to simulate nonequilibrium compressible flows. Compared with the original discrete BGK model, the discrete ES-BGK has a flexible Prandtl number. For the discrete ES-BGK model in the Burnett level, two kinds of discrete velocity model are introduced and the relations between nonequilibrium quantities and the viscous stress and heat flux in the Burnett level are established. The model is verified via four benchmark tests. In addition, a new idea is introduced to recover the actual distribution function through the macroscopic quantities and their space derivatives. The recovery scheme works not only for discrete Boltzmann simulation but also for hydrodynamic ones, for example, those based on the Navier-Stokes or the Burnett equations.
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Kung, Pen-Chen; Yeh, Mei Chang; Lai, Ming-Kuen; Liu, Hsueh-Erh
2017-10-01
Kidney transplant failures are caused primarily by lack of adherence to immunosuppressive medication regimens by patients after transplantation. A number of studies have indicated that health-related beliefs are an effective predictor of health-related behavior. The aim of this study is to understand the influence of the personal characteristics and health-related beliefs of patients on adherence to treatment with immunosuppressive medication based on the Health Belief Model. This cross-sectional study distributed questionnaires to patients who had been recruited via purposive sampling at one medical center in Taipei. All of the potential participants had undergone kidney transplantation at least 6 months previously. The self-developed questionnaire collected data in three areas: personal characteristics, health-related beliefs regarding transplant rejection, and adherence to the immunosuppressive medication regimen. One hundred twenty-two valid questionnaires were received. The collected data were analyzed using descriptive statistics, independent t test, one-way analysis of variance, Pearson's correlation, and multiple regression. Participants who had received dialysis treatment or had experienced rejection perceived susceptibility to rejection more strongly than those who had not. Participants who had undergone transplantation in Taiwan, had experienced more drug-related symptoms, or had contracted severe to extremely severe infections in the past showed lower rates of adherence to treatment with immunosuppressive medication. Adherence to medication regimens correlated negatively with length of time since transplantation. Length of time since transplantation, drug-related symptoms, perceived susceptibility to rejection, and perceived benefits of treatment were identified as major predictors of adherence to immunosuppressive medication regimens. The results partially conformed to the concepts of the Health Belief Model. Perceived susceptibility to rejection and
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Eigenfunction statistics for Anderson model with Hölder continuous ...
Indian Academy of Sciences (India)
The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India ... Anderson model; Hölder continuous measure; Poisson statistics. ...... [4] Combes J-M, Hislop P D and Klopp F, An optimal Wegner estimate and its application to.
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A BRDF statistical model applying to space target materials modeling
Liu, Chenghao; Li, Zhi; Xu, Can; Tian, Qichen
2017-10-01
In order to solve the problem of poor effect in modeling the large density BRDF measured data with five-parameter semi-empirical model, a refined statistical model of BRDF which is suitable for multi-class space target material modeling were proposed. The refined model improved the Torrance-Sparrow model while having the modeling advantages of five-parameter model. Compared with the existing empirical model, the model contains six simple parameters, which can approximate the roughness distribution of the material surface, can approximate the intensity of the Fresnel reflectance phenomenon and the attenuation of the reflected light's brightness with the azimuth angle changes. The model is able to achieve parameter inversion quickly with no extra loss of accuracy. The genetic algorithm was used to invert the parameters of 11 different samples in the space target commonly used materials, and the fitting errors of all materials were below 6%, which were much lower than those of five-parameter model. The effect of the refined model is verified by comparing the fitting results of the three samples at different incident zenith angles in 0° azimuth angle. Finally, the three-dimensional modeling visualizations of these samples in the upper hemisphere space was given, in which the strength of the optical scattering of different materials could be clearly shown. It proved the good describing ability of the refined model at the material characterization as well.
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International Nuclear Information System (INIS)
Hong, Kee Jeung; Kim, Jee Sang
2009-01-01
As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.
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Spatio-temporal statistical models with applications to atmospheric processes
International Nuclear Information System (INIS)
Wikle, C.K.
1996-01-01
This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model
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Statistical mechanics of directed models of polymers in the square lattice
International Nuclear Information System (INIS)
Rensburg, E J Janse van
2003-01-01
Directed square lattice models of polymers and vesicles have received considerable attention in the recent mathematical and physical sciences literature. These are idealized geometric directed lattice models introduced to study phase behaviour in polymers, and include Dyck paths, partially directed paths, directed trees and directed vesicles models. Directed models are closely related to models studied in the combinatorics literature (and are often exactly solvable). They are also simplified versions of a number of statistical mechanics models, including the self-avoiding walk, lattice animals and lattice vesicles. The exchange of approaches and ideas between statistical mechanics and combinatorics have considerably advanced the description and understanding of directed lattice models, and this will be explored in this review. The combinatorial nature of directed lattice path models makes a study using generating function approaches most natural. In contrast, the statistical mechanics approach would introduce partition functions and free energies, and then investigate these using the general framework of critical phenomena. Generating function and statistical mechanics approaches are closely related. For example, questions regarding the limiting free energy may be approached by considering the radius of convergence of a generating function, and the scaling properties of thermodynamic quantities are related to the asymptotic properties of the generating function. In this review the methods for obtaining generating functions and determining free energies in directed lattice path models of linear polymers is presented. These methods include decomposition methods leading to functional recursions, as well as the Temperley method (that is implemented by creating a combinatorial object, one slice at a time). A constant term formulation of the generating function will also be reviewed. The thermodynamic features and critical behaviour in models of directed paths may be
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Interpretation of the results of statistical measurements. [search for basic probability model
Olshevskiy, V. V.
1973-01-01
For random processes, the calculated probability characteristic, and the measured statistical estimate are used in a quality functional, which defines the difference between the two functions. Based on the assumption that the statistical measurement procedure is organized so that the parameters for a selected model are optimized, it is shown that the interpretation of experimental research is a search for a basic probability model.
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An improved mixing model providing joint statistics of scalar and scalar dissipation
Energy Technology Data Exchange (ETDEWEB)
Meyer, Daniel W. [Department of Energy Resources Engineering, Stanford University, Stanford, CA (United States); Jenny, Patrick [Institute of Fluid Dynamics, ETH Zurich (Switzerland)
2008-11-15
For the calculation of nonpremixed turbulent flames with thin reaction zones the joint probability density function (PDF) of the mixture fraction and its dissipation rate plays an important role. The corresponding PDF transport equation involves a mixing model for the closure of the molecular mixing term. Here, the parameterized scalar profile (PSP) mixing model is extended to provide the required joint statistics. Model predictions are validated using direct numerical simulation (DNS) data of a passive scalar mixing in a statistically homogeneous turbulent flow. Comparisons between the DNS and the model predictions are provided, which involve different initial scalar-field lengthscales. (author)
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Statistics of excitations in the electron glass model
Palassini, Matteo
2011-03-01
We study the statistics of elementary excitations in the classical electron glass model of localized electrons interacting via the unscreened Coulomb interaction in the presence of disorder. We reconsider the long-standing puzzle of the exponential suppression of the single-particle density of states near the Fermi level, by measuring accurately the density of states of charged and electron-hole pair excitations via finite temperature Monte Carlo simulation and zero-temperature relaxation. We also investigate the statistics of large charge rearrangements after a perturbation of the system, which may shed some light on the slow relaxation and glassy phenomena recently observed in a variety of Anderson insulators. In collaboration with Martin Goethe.
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Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks
Directory of Open Access Journals (Sweden)
Luciano Pivoto Specht
2007-03-01
Full Text Available It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.
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ARSENIC CONTAMINATION IN GROUNDWATER: A STATISTICAL MODELING
Palas Roy; Naba Kumar Mondal; Biswajit Das; Kousik Das
2013-01-01
High arsenic in natural groundwater in most of the tubewells of the Purbasthali- Block II area of Burdwan district (W.B, India) has recently been focused as a serious environmental concern. This paper is intending to illustrate the statistical modeling of the arsenic contaminated groundwater to identify the interrelation of that arsenic contain with other participating groundwater parameters so that the arsenic contamination level can easily be predicted by analyzing only such parameters. Mul...
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Modeling the basic superconductor thermodynamical-statistical characteristics
International Nuclear Information System (INIS)
Palenskis, V.; Maknys, K.
1999-01-01
In accordance with the Landau second-order phase transition and other thermodynamical-statistical relations for superconductors, and using the energy gap as an order parameter in the electron free energy presentation, the fundamental characteristics of electrons, such as the free energy, the total energy, the energy gap, the entropy, and the heat capacity dependences on temperature were obtained. The obtained modeling results, in principle, well reflect the basic low- and high-temperature superconductor characteristics
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Improved air ventilation rate estimation based on a statistical model
International Nuclear Information System (INIS)
Brabec, M.; Jilek, K.
2004-01-01
A new approach to air ventilation rate estimation from CO measurement data is presented. The approach is based on a state-space dynamic statistical model, allowing for quick and efficient estimation. Underlying computations are based on Kalman filtering, whose practical software implementation is rather easy. The key property is the flexibility of the model, allowing various artificial regimens of CO level manipulation to be treated. The model is semi-parametric in nature and can efficiently handle time-varying ventilation rate. This is a major advantage, compared to some of the methods which are currently in practical use. After a formal introduction of the statistical model, its performance is demonstrated on real data from routine measurements. It is shown how the approach can be utilized in a more complex situation of major practical relevance, when time-varying air ventilation rate and radon entry rate are to be estimated simultaneously from concurrent radon and CO measurements
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Appplication of statistical mechanical methods to the modeling of social networks
Strathman, Anthony Robert
With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.
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Statistical 3D damage accumulation model for ion implant simulators
Hernandez-Mangas, J M; Enriquez, L E; Bailon, L; Barbolla, J; Jaraiz, M
2003-01-01
A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided.
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Statistical 3D damage accumulation model for ion implant simulators
International Nuclear Information System (INIS)
Hernandez-Mangas, J.M.; Lazaro, J.; Enriquez, L.; Bailon, L.; Barbolla, J.; Jaraiz, M.
2003-01-01
A statistical 3D damage accumulation model, based on the modified Kinchin-Pease formula, for ion implant simulation has been included in our physically based ion implantation code. It has only one fitting parameter for electronic stopping and uses 3D electron density distributions for different types of targets including compound semiconductors. Also, a statistical noise reduction mechanism based on the dose division is used. The model has been adapted to be run under parallel execution in order to speed up the calculation in 3D structures. Sequential ion implantation has been modelled including previous damage profiles. It can also simulate the implantation of molecular and cluster projectiles. Comparisons of simulated doping profiles with experimental SIMS profiles are presented. Also comparisons between simulated amorphization and experimental RBS profiles are shown. An analysis of sequential versus parallel processing is provided
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O'Mara, Deborah A; Canny, Ben J; Rothnie, Imogene P; Wilson, Ian G; Barnard, John; Davies, Llewelyn
2015-02-02
To report the level of participation of medical schools in the Australian Medical Schools Assessment Collaboration (AMSAC); and to measure differences in student performance related to medical school characteristics and implementation methods. Retrospective analysis of data using the Rasch statistical model to correct for missing data and variability in item difficulty. Linear model analysis of variance was used to assess differences in student performance. 6401 preclinical students from 13 medical schools that participated in AMSAC from 2011 to 2013. Rasch estimates of preclinical basic and clinical science knowledge. Representation of Australian medical schools and students in AMSAC more than doubled between 2009 and 2013. In 2013 it included 12 of 19 medical schools and 68% of medical students. Graduate-entry students scored higher than students entering straight from school. Students at large schools scored higher than students at small schools. Although the significance level was high (P performance. The effect on performance of multiple assessments compared with the test items as part of a single end-of-year examination was negligible. The variables investigated explain only 12% of the total variation in student performance. An increasing number of medical schools are participating in AMSAC to monitor student performance in preclinical sciences against an external benchmark. Medical school characteristics account for only a small part of overall variation in student performance. Student performance was not affected by the different methods of administering test items.
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Statistical modelling of space-time processes with application to wind power
DEFF Research Database (Denmark)
Lenzi, Amanda
. This thesis aims at contributing to the wind power literature by building and evaluating new statistical techniques for producing forecasts at multiple locations and lead times using spatio-temporal information. By exploring the features of a rich portfolio of wind farms in western Denmark, we investigate...... propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial...
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Rohrmeier, Martin A; Cross, Ian
2014-07-01
Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.
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Statistical power of model selection strategies for genome-wide association studies.
Directory of Open Access Journals (Sweden)
Zheyang Wu
2009-07-01
Full Text Available Genome-wide association studies (GWAS aim to identify genetic variants related to diseases by examining the associations between phenotypes and hundreds of thousands of genotyped markers. Because many genes are potentially involved in common diseases and a large number of markers are analyzed, it is crucial to devise an effective strategy to identify truly associated variants that have individual and/or interactive effects, while controlling false positives at the desired level. Although a number of model selection methods have been proposed in the literature, including marginal search, exhaustive search, and forward search, their relative performance has only been evaluated through limited simulations due to the lack of an analytical approach to calculating the power of these methods. This article develops a novel statistical approach for power calculation, derives accurate formulas for the power of different model selection strategies, and then uses the formulas to evaluate and compare these strategies in genetic model spaces. In contrast to previous studies, our theoretical framework allows for random genotypes, correlations among test statistics, and a false-positive control based on GWAS practice. After the accuracy of our analytical results is validated through simulations, they are utilized to systematically evaluate and compare the performance of these strategies in a wide class of genetic models. For a specific genetic model, our results clearly reveal how different factors, such as effect size, allele frequency, and interaction, jointly affect the statistical power of each strategy. An example is provided for the application of our approach to empirical research. The statistical approach used in our derivations is general and can be employed to address the model selection problems in other random predictor settings. We have developed an R package markerSearchPower to implement our formulas, which can be downloaded from the
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Decoding β-decay systematics: A global statistical model for β- half-lives
International Nuclear Information System (INIS)
Costiris, N. J.; Mavrommatis, E.; Gernoth, K. A.; Clark, J. W.
2009-01-01
Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we implement advances in training algorithms designed to improve generalization, in application to the problem of reproducing and predicting the half-lives of nuclear ground states that decay 100% by the β - mode. More specifically, fully connected, multilayer feed-forward artificial neural network models are developed using the Levenberg-Marquardt optimization algorithm together with Bayesian regularization and cross-validation. The predictive performance of models emerging from extensive computer experiments is compared with that of traditional microscopic and phenomenological models as well as with the performance of other learning systems, including earlier neural network models as well as the support vector machines recently applied to the same problem. In discussing the results, emphasis is placed on predictions for nuclei that are far from the stability line, and especially those involved in r-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for β-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape.
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Warriner, David Roy; Bayley, Martin; Shi, Yubing; Lawford, Patricia Victoria; Narracott, Andrew; Fenner, John
2017-11-21
This study combined themes in cardiovascular modelling, clinical cardiology and e-learning to create an on-line environment that would assist undergraduate medical students in understanding key physiological and pathophysiological processes in the cardiovascular system. An interactive on-line environment was developed incorporating a lumped-parameter mathematical model of the human cardiovascular system. The model outputs were used to characterise the progression of key disease processes and allowed students to classify disease severity with the aim of improving their understanding of abnormal physiology in a clinical context. Access to the on-line environment was offered to students at all stages of undergraduate training as an adjunct to routine lectures and tutorials in cardiac pathophysiology. Student feedback was collected on this novel on-line material in the course of routine audits of teaching delivery. Medical students, irrespective of their stage of undergraduate training, reported that they found the models and the environment interesting and a positive experience. After exposure to the environment, there was a statistically significant improvement in student performance on a series of 6 questions based on cardiovascular medicine, with a 33% and 22% increase in the number of questions answered correctly, p < 0.0001 and p < 0.001 respectively. Considerable improvement was found in students' knowledge and understanding during assessment after exposure to the e-learning environment. Opportunities exist for development of similar environments in other fields of medicine, refinement of the existing environment and further engagement with student cohorts. This work combines some exciting and developing fields in medical education, but routine adoption of these types of tool will be possible only with the engagement of all stake-holders, from educationalists, clinicians, modellers to, most importantly, medical students.
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Directory of Open Access Journals (Sweden)
David Roy Warriner
2017-11-01
Full Text Available Abstract Background This study combined themes in cardiovascular modelling, clinical cardiology and e-learning to create an on-line environment that would assist undergraduate medical students in understanding key physiological and pathophysiological processes in the cardiovascular system. Methods An interactive on-line environment was developed incorporating a lumped-parameter mathematical model of the human cardiovascular system. The model outputs were used to characterise the progression of key disease processes and allowed students to classify disease severity with the aim of improving their understanding of abnormal physiology in a clinical context. Access to the on-line environment was offered to students at all stages of undergraduate training as an adjunct to routine lectures and tutorials in cardiac pathophysiology. Student feedback was collected on this novel on-line material in the course of routine audits of teaching delivery. Results Medical students, irrespective of their stage of undergraduate training, reported that they found the models and the environment interesting and a positive experience. After exposure to the environment, there was a statistically significant improvement in student performance on a series of 6 questions based on cardiovascular medicine, with a 33% and 22% increase in the number of questions answered correctly, p < 0.0001 and p < 0.001 respectively. Conclusions Considerable improvement was found in students’ knowledge and understanding during assessment after exposure to the e-learning environment. Opportunities exist for development of similar environments in other fields of medicine, refinement of the existing environment and further engagement with student cohorts. This work combines some exciting and developing fields in medical education, but routine adoption of these types of tool will be possible only with the engagement of all stake-holders, from educationalists, clinicians, modellers to
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Two-dimensional models in statistical mechanics and field theory
International Nuclear Information System (INIS)
Koberle, R.
1980-01-01
Several features of two-dimensional models in statistical mechanics and Field theory, such as, lattice quantum chromodynamics, Z(N), Gross-Neveu and CP N-1 are discussed. The problems of confinement and dynamical mass generation are also analyzed. (L.C.) [pt
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Syntactic discriminative language model rerankers for statistical machine translation
Carter, S.; Monz, C.
2011-01-01
This article describes a method that successfully exploits syntactic features for n-best translation candidate reranking using perceptrons. We motivate the utility of syntax by demonstrating the superior performance of parsers over n-gram language models in differentiating between Statistical
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Factors Affecting the Choice of Professors as a Role Model from the Viewpoint of Medical Students
Directory of Open Access Journals (Sweden)
Masome Rahimi
2018-01-01
Full Text Available The role of professors as a model can have a beneficial impact on the mental, psychological and educational conditions of medical students. This also plays an important role in improving professionalism and academic achievements among medical students. Therefore, the present study was aimed at evaluating the standpoint of students on factors influencing the selection of professors as a role model. This descriptive cross-sectional study was conducted on the students of different disciplines studying in Jahrom University of Medical Sciences in 2016. A randomized sampling method was conducted on 217 students. Their viewpoints were collected using a 30- question researcher-made questionnaire. The questionnaire consisted of three parts, each containing ten items. In addition, this questionnaire was distributed among 20 people (as a pilot survey, the alpha coefficient of which was equal to 0.88; and its measurement was based on Likert scale "from very low to very high". Data were analyzed using SPSS 18 and descriptive statistics. Most respondents were nursing students and the highest influence of professors as a role model was associated with their role as a research leader (future specialized courses in the clinical choices and selection of future specialized fields. The factors influencing the selection of professors as a role model included their respectful attitude toward students, and the high level of their knowledge and skills. On the other hand, the most important factors that caused professors not to be regarded as a role model included their inappropriate relationship with the students and refusing to listen to them. Role model professors can have a beneficial impact on the future of students and scientific communities, as far as the science and education is concerned. Therefore, it is necessary for professors to pay particular attention to strengthening their role as a model at universities.
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Monte Carlo based statistical power analysis for mediation models: methods and software.
Zhang, Zhiyong
2014-12-01
The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.
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Statistical Modelling of Synaptic Vesicles Distribution and Analysing their Physical Characteristics
DEFF Research Database (Denmark)
Khanmohammadi, Mahdieh
transmission electron microscopy is used to acquire images from two experimental groups of rats: 1) rats subjected to a behavioral model of stress and 2) rats subjected to sham stress as the control group. The synaptic vesicle distribution and interactions are modeled by employing a point process approach......This Ph.D. thesis deals with mathematical and statistical modeling of synaptic vesicle distribution, shape, orientation and interactions. The first major part of this thesis treats the problem of determining the effect of stress on synaptic vesicle distribution and interactions. Serial section...... on differences of statistical measures in section and the same measures in between sections. Three-dimensional (3D) datasets are reconstructed by using image registration techniques and estimated thicknesses. We distinguish the effect of stress by estimating the synaptic vesicle densities and modeling...
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Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J
2014-01-01
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
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Wanted: role models - medical students’ perceptions of professionalism
Directory of Open Access Journals (Sweden)
Byszewski Anna
2012-11-01
Full Text Available Abstract Background Transformation of medical students to become medical professionals is a core competency required for physicians in the 21st century. Role modeling was traditionally the key method of transmitting this skill. Medical schools are developing medical curricula which are explicit in ensuring students develop the professional competency and understand the values and attributes of this role. The purpose of this study was to determine student perception of professionalism at the University of Ottawa and gain insights for improvement in promotion of professionalism in undergraduate medical education. Methods Survey on student perception of professionalism in general, the curriculum and learning environment at the University of Ottawa, and the perception of student behaviors, was developed by faculty and students and sent electronically to all University of Ottawa medical students. The survey included both quantitative items including an adapted Pritzker list and qualitative responses to eight open ended questions on professionalism at the Faculty of Medicine, University of Ottawa. All analyses were performed using SAS version 9.1 (SAS Institute Inc. Cary, NC, USA. Chi-square and Fischer’s exact test (for cell count less than 5 were used to derive p-values for categorical variables by level of student learning. Results The response rate was 45.6% (255 of 559 students for all four years of the curriculum. 63% of the responses were from students in years 1 and 2 (preclerkship. Students identified role modeling as the single most important aspect of professionalism. The strongest curricular recommendations included faculty-led case scenario sessions, enhancing interprofessional interactions and the creation of special awards to staff and students to “celebrate” professionalism. Current evaluation systems were considered least effective. The importance of role modeling and information on how to report lapses and breaches was
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A statistical mechanics model for free-for-all airplane passenger boarding
Steffen, Jason H.
2008-12-01
I discuss a model for free-for-all passenger boarding which is employed by some discount air carriers. The model is based on the principles of statistical mechanics, where each seat in the aircraft has an associated energy which reflects the preferences of travelers. As each passenger enters the airplane they select their seats using Boltzmann statistics, proceed to that location, load their luggage, sit down, and the partition function seen by remaining passengers is modified to reflect this fact. I discuss the various model parameters and make qualitative comparisons of this passenger boarding model with those that involve assigned seats. The model can be used to predict the probability that certain seats will be occupied at different times during the boarding process. These results might provide a useful description of this boarding method. The model is a relatively unusual application of undergraduate level physics and describes a situation familiar to many students and faculty.
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A statistical mechanics model for free-for-all airplane passenger boarding
International Nuclear Information System (INIS)
Steffen, Jason H.; Fermilab
2008-01-01
I discuss a model for free-for-all passenger boarding which is employed by some discount air carriers. The model is based on the principles of statistical mechanics where each seat in the aircraft has an associated energy which reflects the preferences of travelers. As each passenger enters the airplane they select their seats using Boltzmann statistics, proceed to that location, load their luggage, sit down, and the partition function seen by remaining passengers is modified to reflect this fact. I discuss the various model parameters and make qualitative comparisons of this passenger boarding model with those that involve assigned seats. The model can be used to predict the probability that certain seats will be occupied at different times during the boarding process. These results might provide a useful description of this boarding method. The model is a relatively unusual application of undergraduate level physics and describes a situation familiar to many students and faculty
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A statistical mechanics model for free-for-all airplane passenger boarding
Energy Technology Data Exchange (ETDEWEB)
Steffen, Jason H.; /Fermilab
2008-08-01
I discuss a model for free-for-all passenger boarding which is employed by some discount air carriers. The model is based on the principles of statistical mechanics where each seat in the aircraft has an associated energy which reflects the preferences of travelers. As each passenger enters the airplane they select their seats using Boltzmann statistics, proceed to that location, load their luggage, sit down, and the partition function seen by remaining passengers is modified to reflect this fact. I discuss the various model parameters and make qualitative comparisons of this passenger boarding model with those that involve assigned seats. The model can be used to predict the probability that certain seats will be occupied at different times during the boarding process. These results might provide a useful description of this boarding method. The model is a relatively unusual application of undergraduate level physics and describes a situation familiar to many students and faculty.
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A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.
Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong; Yan, Jin; Wu, Guorong
2017-06-01
Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity. In addition to the low level region-to-region signal correlation, latent module-to-module connection is also estimated and used to provide high level heuristics for measuring connectivity strength. Furthermore, sparsity constraint is employed to automatically remove the spurious connections, thus alleviating the issue of searching for optimal threshold. Second, we integrate our learning-based approach with the sliding-window technique to further reveal the dynamics of functional connectivity. Specifically, we stack the functional connectivity matrix within each sliding window and form a 3D tensor where the third dimension denotes for time. Then we obtain dynamic functional connectivity (dFC) for each individual subject by simultaneously estimating the within-sliding-window functional connectivity and characterizing the across-sliding-window temporal dynamics. Third, in order to enhance the robustness of the connectome patterns extracted from dFC, we extend the individual-based 3D tensors to a population-based 4D tensor (with the fourth dimension stands for the training subjects) and learn the statistics of connectome patterns via 4D tensor analysis. Since our 4D tensor model jointly (1) optimizes dFC for each training subject and (2) captures the principle connectome patterns, our statistical model gains more statistical power of representing new subject than current state
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Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models
Directory of Open Access Journals (Sweden)
Seyed Mehran Kazemi
2018-02-01
Full Text Available The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship among them or combining them. In this article, we study the relationship between the path ranking algorithm (PRA, one of the most well-known relational learning methods in the graph random walk paradigm, and relational logistic regression (RLR, one of the recent developments in weighted rule learning. We provide a simple way to normalize relations and prove that relational logistic regression using normalized relations generalizes the path ranking algorithm. This result provides a better understanding of relational learning, especially for the weighted rule learning and graph random walk paradigms. It opens up the possibility of using the more flexible RLR rules within PRA models and even generalizing both by including normalized and unnormalized relations in the same model.
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Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.
2017-01-01
Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable
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Pseudo-dynamic source modelling with 1-point and 2-point statistics of earthquake source parameters
Song, S. G.
2013-12-24
Ground motion prediction is an essential element in seismic hazard and risk analysis. Empirical ground motion prediction approaches have been widely used in the community, but efficient simulation-based ground motion prediction methods are needed to complement empirical approaches, especially in the regions with limited data constraints. Recently, dynamic rupture modelling has been successfully adopted in physics-based source and ground motion modelling, but it is still computationally demanding and many input parameters are not well constrained by observational data. Pseudo-dynamic source modelling keeps the form of kinematic modelling with its computational efficiency, but also tries to emulate the physics of source process. In this paper, we develop a statistical framework that governs the finite-fault rupture process with 1-point and 2-point statistics of source parameters in order to quantify the variability of finite source models for future scenario events. We test this method by extracting 1-point and 2-point statistics from dynamically derived source models and simulating a number of rupture scenarios, given target 1-point and 2-point statistics. We propose a new rupture model generator for stochastic source modelling with the covariance matrix constructed from target 2-point statistics, that is, auto- and cross-correlations. Our sensitivity analysis of near-source ground motions to 1-point and 2-point statistics of source parameters provides insights into relations between statistical rupture properties and ground motions. We observe that larger standard deviation and stronger correlation produce stronger peak ground motions in general. The proposed new source modelling approach will contribute to understanding the effect of earthquake source on near-source ground motion characteristics in a more quantitative and systematic way.
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Modeling determinants of medication attitudes and poor adherence in early nonaffective psychosis
DEFF Research Database (Denmark)
Drake, Richard J; Nordentoft, Merete; Haddock, Gillian
2015-01-01
the hypothesis that medication attitudes, while meaningfully different from "insight," correlated with insight and self-esteem, and change in each influenced the others. Rosenberg Self-Esteem Scale, Birchwood Insight Scale, and Positive and Negative Syndrome Scale insight were assessed at presentation, after 6...... medication attitudes, self-esteem, and insight in an early psychosis cohort better to understand their relationships. Existing longitudinal data from 309 patients with early Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, nonaffective psychosis (83% first episode) were analyzed to test...... five latent constructs best fitted the data: medication attitudes, self-esteem, accepting need for treatment, self-rated insight, and objective insight. All were related and each affected the others as it changed, except self-esteem and medication attitudes. Low self-reported insight at presentation...
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Mousavi, Seyed Mohammad Hadi; Dargahi, Hossein; Mohammadi, Sara
2016-10-01
Creating a safe of health care system requires the establishment of High Reliability Organizations (HROs), which reduces errors, and increases the level of safety in hospitals. This model focuses on improving reliability through higher process design, building a culture of accreditation, and leveraging human factors. The present study intends to determine the readiness of hospitals for the establishment of HROs model in Tehran University of Medical Sciences from the viewpoint of managers of these hospitals. This is a descriptive-analytical study carried out in 2013-2014. The research population consists of 105 senior and middle managers of 15 hospitals of Tehran University of Medical Sciences. The data collection tool was a 55-question researcher-made questionnaire, included six elements of HROs to assess the level of readiness for establishing HROS model from managers' point of view. The validity of the questionnaire was calculated through the content validity method using 10 experts in the area of hospitals' accreditation, and its reliability was calculated through test-retest method with a correlation coefficient of 0.90. The response rate was 90 percent. The Likert scale was used for the questions, and data analysis was conducted through SPSS version 21 Descriptive statistics was presented via tables and normal distributions of data and means. Analytical methods, including t-test, Mann-Whitney, Spearman, and Kruskal-Wallis, were used for presenting inferential statistics. The study showed that from the viewpoint of senior and middle managers of the hospitals considered in this study, these hospitals are indeed ready for acceptance and establishment of HROs model. A significant relationship was showed between HROs model and its elements with demographic details of managers like their age, work experience, management experience, and level of management. Although the studied hospitals, as viewed by their managers, are capable of attaining the goals of HROs, it
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Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models
International Nuclear Information System (INIS)
Andrade, A.R.; Teixeira, P.F.
2015-01-01
Railway maintenance planners require a predictive model that can assess the railway track geometry degradation. The present paper uses a Hierarchical Bayesian model as a tool to model the main two quality indicators related to railway track geometry degradation: the standard deviation of longitudinal level defects and the standard deviation of horizontal alignment defects. Hierarchical Bayesian Models (HBM) are flexible statistical models that allow specifying different spatially correlated components between consecutive track sections, namely for the deterioration rates and the initial qualities parameters. HBM are developed for both quality indicators, conducting an extensive comparison between candidate models and a sensitivity analysis on prior distributions. HBM is applied to provide an overall assessment of the degradation of railway track geometry, for the main Portuguese railway line Lisbon–Oporto. - Highlights: • Rail track geometry degradation is analysed using Hierarchical Bayesian models. • A Gibbs sampling strategy is put forward to estimate the HBM. • Model comparison and sensitivity analysis find the most suitable model. • We applied the most suitable model to all the segments of the main Portuguese line. • Tackling spatial correlations using CAR structures lead to a better model fit
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Nearing, G. S.
2014-12-01
Statistical models consistently out-perform conceptual models in the short term, however to account for a nonstationary future (or an unobserved past) scientists prefer to base predictions on unchanging and commutable properties of the universe - i.e., physics. The problem with physically-based hydrology models is, of course, that they aren't really based on physics - they are based on statistical approximations of physical interactions, and we almost uniformly lack an understanding of the entropy associated with these approximations. Thermodynamics is successful precisely because entropy statistics are computable for homogeneous (well-mixed) systems, and ergodic arguments explain the success of Newton's laws to describe systems that are fundamentally quantum in nature. Unfortunately, similar arguments do not hold for systems like watersheds that are heterogeneous at a wide range of scales. Ray Solomonoff formalized the situation in 1968 by showing that given infinite evidence, simultaneously minimizing model complexity and entropy in predictions always leads to the best possible model. The open question in hydrology is about what happens when we don't have infinite evidence - for example, when the future will not look like the past, or when one watershed does not behave like another. How do we isolate stationary and commutable components of watershed behavior? I propose that one possible answer to this dilemma lies in a formal combination of physics and statistics. In this talk I outline my recent analogue (Solomonoff's theorem was digital) of Solomonoff's idea that allows us to quantify the complexity/entropy tradeoff in a way that is intuitive to physical scientists. I show how to formally combine "physical" and statistical methods for model development in a way that allows us to derive the theoretically best possible model given any given physics approximation(s) and available observations. Finally, I apply an analogue of Solomonoff's theorem to evaluate the
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A statistical model for interpreting computerized dynamic posturography data
Feiveson, Alan H.; Metter, E. Jeffrey; Paloski, William H.
2002-01-01
Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.
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Using statistical compatibility to derive advanced probabilistic fatigue models
Czech Academy of Sciences Publication Activity Database
Fernández-Canteli, A.; Castillo, E.; López-Aenlle, M.; Seitl, Stanislav
2010-01-01
Roč. 2, č. 1 (2010), s. 1131-1140 E-ISSN 1877-7058. [Fatigue 2010. Praha, 06.06.2010-11.06.2010] Institutional research plan: CEZ:AV0Z20410507 Keywords : Fatigue models * Statistical compatibility * Functional equations Subject RIV: JL - Materials Fatigue, Friction Mechanics
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Optimizing refiner operation with statistical modelling
Energy Technology Data Exchange (ETDEWEB)
Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)
1997-02-01
The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.
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The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.
Tendeiro, Jorge N
2017-01-01
Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.
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Quantum statistical model of nuclear multifragmentation in the canonical ensemble method
International Nuclear Information System (INIS)
Toneev, V.D.; Ploszajczak, M.; Parvant, A.S.; Toneev, V.D.; Parvant, A.S.
1999-01-01
A quantum statistical model of nuclear multifragmentation is proposed. The recurrence equation method used the canonical ensemble makes the model solvable and transparent to physical assumptions and allows to get results without involving the Monte Carlo technique. The model exhibits the first order phase transition. Quantum statistics effects are clearly seen on the microscopic level of occupation numbers but are almost washed out for global thermodynamic variables and the averaged observables studied. In the latter case, the recurrence relations for multiplicity distributions of both intermediate-mass and all fragments are derived and the specific changes in the shape of multiplicity distributions in the narrow region of the transition temperature is stressed. The temperature domain favorable to search for the HBT effect is noted. (authors)
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Quantum statistical model of nuclear multifragmentation in the canonical ensemble method
Energy Technology Data Exchange (ETDEWEB)
Toneev, V.D.; Ploszajczak, M. [Grand Accelerateur National d' Ions Lourds (GANIL), 14 - Caen (France); Parvant, A.S. [Institute of Applied Physics, Moldova Academy of Sciences, MD Moldova (Ukraine); Parvant, A.S. [Joint Institute for Nuclear Research, Bogoliubov Lab. of Theoretical Physics, Dubna (Russian Federation)
1999-07-01
A quantum statistical model of nuclear multifragmentation is proposed. The recurrence equation method used the canonical ensemble makes the model solvable and transparent to physical assumptions and allows to get results without involving the Monte Carlo technique. The model exhibits the first order phase transition. Quantum statistics effects are clearly seen on the microscopic level of occupation numbers but are almost washed out for global thermodynamic variables and the averaged observables studied. In the latter case, the recurrence relations for multiplicity distributions of both intermediate-mass and all fragments are derived and the specific changes in the shape of multiplicity distributions in the narrow region of the transition temperature is stressed. The temperature domain favorable to search for the HBT effect is noted. (authors)
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Non-Gaussianity and statistical anisotropy from vector field populated inflationary models
Dimastrogiovanni, Emanuela; Matarrese, Sabino; Riotto, Antonio
2010-01-01
We present a review of vector field models of inflation and, in particular, of the statistical anisotropy and non-Gaussianity predictions of models with SU(2) vector multiplets. Non-Abelian gauge groups introduce a richer amount of predictions compared to the Abelian ones, mostly because of the presence of vector fields self-interactions. Primordial vector fields can violate isotropy leaving their imprint in the comoving curvature fluctuations zeta at late times. We provide the analytic expressions of the correlation functions of zeta up to fourth order and an analysis of their amplitudes and shapes. The statistical anisotropy signatures expected in these models are important and, potentially, the anisotropic contributions to the bispectrum and the trispectrum can overcome the isotropic parts.
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Computational algebraic geometry for statistical modeling FY09Q2 progress.
Energy Technology Data Exchange (ETDEWEB)
Thompson, David C.; Rojas, Joseph Maurice; Pebay, Philippe Pierre
2009-03-01
This is a progress report on polynomial system solving for statistical modeling. This is a progress report on polynomial system solving for statistical modeling. This quarter we have developed our first model of shock response data and an algorithm for identifying the chamber cone containing a polynomial system in n variables with n+k terms within polynomial time - a significant improvement over previous algorithms, all having exponential worst-case complexity. We have implemented and verified the chamber cone algorithm for n+3 and are working to extend the implementation to handle arbitrary k. Later sections of this report explain chamber cones in more detail; the next section provides an overview of the project and how the current progress fits into it.
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Development of 3D statistical mandible models for cephalometric measurements
International Nuclear Information System (INIS)
Kim, Sung Goo; Yi, Won Jin; Hwang, Soon Jung; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il; Hong, Helen; Yoo, Ji Hyun
2012-01-01
The aim of this study was to provide sex-matched three-dimensional (3D) statistical shape models of the mandible, which would provide cephalometric parameters for 3D treatment planning and cephalometric measurements in orthognathic surgery. The subjects used to create the 3D shape models of the mandible included 23 males and 23 females. The mandibles were segmented semi-automatically from 3D facial CT images. Each individual mandible shape was reconstructed as a 3D surface model, which was parameterized to establish correspondence between different individual surfaces. The principal component analysis (PCA) applied to all mandible shapes produced a mean model and characteristic models of variation. The cephalometric parameters were measured directly from the mean models to evaluate the 3D shape models. The means of the measured parameters were compared with those from other conventional studies. The male and female 3D statistical mean models were developed from 23 individual mandibles, respectively. The male and female characteristic shapes of variation produced by PCA showed a large variability included in the individual mandibles. The cephalometric measurements from the developed models were very close to those from some conventional studies. We described the construction of 3D mandibular shape models and presented the application of the 3D mandibular template in cephalometric measurements. Optimal reference models determined from variations produced by PCA could be used for craniofacial patients with various types of skeletal shape.
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Development of 3D statistical mandible models for cephalometric measurements
Energy Technology Data Exchange (ETDEWEB)
Kim, Sung Goo; Yi, Won Jin; Hwang, Soon Jung; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il [School of Dentistry, Seoul National University, Seoul (Korea, Republic of); Hong, Helen; Yoo, Ji Hyun [Division of Multimedia Engineering, Seoul Women' s University, Seoul (Korea, Republic of)
2012-09-15
The aim of this study was to provide sex-matched three-dimensional (3D) statistical shape models of the mandible, which would provide cephalometric parameters for 3D treatment planning and cephalometric measurements in orthognathic surgery. The subjects used to create the 3D shape models of the mandible included 23 males and 23 females. The mandibles were segmented semi-automatically from 3D facial CT images. Each individual mandible shape was reconstructed as a 3D surface model, which was parameterized to establish correspondence between different individual surfaces. The principal component analysis (PCA) applied to all mandible shapes produced a mean model and characteristic models of variation. The cephalometric parameters were measured directly from the mean models to evaluate the 3D shape models. The means of the measured parameters were compared with those from other conventional studies. The male and female 3D statistical mean models were developed from 23 individual mandibles, respectively. The male and female characteristic shapes of variation produced by PCA showed a large variability included in the individual mandibles. The cephalometric measurements from the developed models were very close to those from some conventional studies. We described the construction of 3D mandibular shape models and presented the application of the 3D mandibular template in cephalometric measurements. Optimal reference models determined from variations produced by PCA could be used for craniofacial patients with various types of skeletal shape.
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A Statistical Model for Synthesis of Detailed Facial Geometry
Golovinskiy, Aleksey; Matusik, Wojciech; Pfister, Hanspeter; Rusinkiewicz, Szymon; Funkhouser, Thomas
2006-01-01
Detailed surface geometry contributes greatly to the visual realism of 3D face models. However, acquiring high-resolution face geometry is often tedious and expensive. Consequently, most face models used in games, virtual reality, or computer vision look unrealistically smooth. In this paper, we introduce a new statistical technique for the analysis and synthesis of small three-dimensional facial features, such as wrinkles and pores. We acquire high-resolution face geometry for people across ...
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Some remarks on the statistical model of heavy ion collisions
International Nuclear Information System (INIS)
Koch, V.
2003-01-01
This contribution is an attempt to assess what can be learned from the remarkable success of this statistical model in describing ratios of particle abundances in ultra-relativistic heavy ion collisions
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International Nuclear Information System (INIS)
Lü, Xiaoshu; Lu, Tao; Kibert, Charles J.; Viljanen, Martti
2015-01-01
Highlights: • This paper presents a new modeling method to forecast energy demands. • The model is based on physical–statistical approach to improving forecast accuracy. • A new method is proposed to address the heterogeneity challenge. • Comparison with measurements shows accurate forecasts of the model. • The first physical–statistical/heterogeneous building energy modeling approach is proposed and validated. - Abstract: Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy
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DEFF Research Database (Denmark)
Baka, N.; Kaptein, B. L.; de Bruijne, Marleen
2011-01-01
Three-dimensional patient specific bone models are required in a range of medical applications, such as pre-operative surgery planning and improved guidance during surgery, modeling and simulation, and in vivo bone motion tracking. Shape reconstruction from a small number of X-ray images is desired...... as it lowers both the acquisition costs and the radiation dose compared to CT. We propose a method for pose estimation and shape reconstruction of 3D bone surfaces from two (or more) calibrated X-ray images using a statistical shape model (SSM). User interaction is limited to manual initialization of the mean...... pose estimation of ground truth shapes as well as 3D shape estimation using a SSM of the whole femur, from stereo cadaver X-rays, in vivo biplane fluoroscopy image-pairs, and an in vivo biplane fluoroscopic sequence. Ground truth shapes for all experiments were available in the form of CT segmentations...
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Rényi statistics for testing composite hypotheses in general exponential models
Czech Academy of Sciences Publication Activity Database
Morales, D.; Pardo, L.; Pardo, M. C.; Vajda, Igor
2004-01-01
Roč. 38, č. 2 (2004), s. 133-147 ISSN 0233-1888 R&D Projects: GA ČR GA201/02/1391 Grant - others:BMF(ES) 2003-00892; BMF(ES) 2003-04820 Institutional research plan: CEZ:AV0Z1075907 Keywords : natural exponential models * Levy processes * generalized Wald statistics Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.323, year: 2004
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Statistical Property and Model for the Inter-Event Time of Terrorism Attacks
Zhu, Jun-Fang; Han, Xiao-Pu; Wang, Bing-Hong
2010-06-01
The inter-event time of terrorism attack events is investigated by empirical data and model analysis. Empirical evidence shows that it follows a scale-free property. In order to understand the dynamic mechanism of such a statistical feature, an opinion dynamic model with a memory effect is proposed on a two-dimensional lattice network. The model mainly highlights the role of individual social conformity and self-affirmation psychology. An attack event occurs when the order parameter indicating the strength of public opposition opinion is smaller than a critical value. Ultimately, the model can reproduce the same statistical property as the empirical data and gives a good understanding for the possible dynamic mechanism of terrorism attacks.
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Application of the statistical approach in diagnosing in medical and biological researches
Directory of Open Access Journals (Sweden)
Komleva N. О.
2017-09-01
Full Text Available The task of diagnosis in biomedical research in a number of cases can be solved using a statistical approach. Current research is the possibility of using statistical analysis to diagnose the state of the human respiratory system based on the values of the percentage contributions of particles of different sizes contained in the exhaled air. The aim of the research is to identify certain regularities in the values of the diagnostic signs of the moisture condensation of the exhaled air, which will make it possible to consider the groups under investigation as disjoint classes. Three groups of individuals were examined: healthy people and patients with bronchitis and pneumonia. For each group, the identification of the particles that are the primary diagnostic data using the laser correlation spectroscopy method and the further processing of the data using the discriminant analysis method are performed. Selection of variables discriminating the study groups in the best possible manner is done; the model of variables and classification functions is constructed. There are presented the results of the main steps of the analysis – the set of variables included in the model and the coefficients of the classification functions for the three groups – which formed the basis for the algorithm for the work of the developed software product.
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Estimating Predictive Variance for Statistical Gas Distribution Modelling
International Nuclear Information System (INIS)
Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo
2009-01-01
Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.
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A combined statistical model for multiple motifs search
International Nuclear Information System (INIS)
Gao Lifeng; Liu Xin; Guan Shan
2008-01-01
Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with definite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite different from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for finding over-represented short sequence segments in different kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from different species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite different, the results show that this model is rather general and sensible. (general)
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Directory of Open Access Journals (Sweden)
Gina Belmonte
2016-07-01
Full Text Available Recent research on formal verification for Collective Adaptive Systems (CAS pushed advancements in spatial and spatio-temporal model checking, and as a side result provided novel image analysis methodologies, rooted in logical methods for topological spaces. Medical Imaging (MI is a field where such technologies show potential for ground-breaking innovation. In this position paper, we present a preliminary investigation centred on applications of spatial model checking to MI. The focus is shifted from pure logics to a mixture of logical, statistical and algorithmic approaches, driven by the logical nature intrinsic to the specification of the properties of interest in the field. As a result, novel operators are introduced, that could as well be brought back to the setting of CAS.
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[Research progress of multi-model medical image fusion and recognition].
Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian
2013-10-01
Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.
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Statistical-mechanical lattice models for protein-DNA binding in chromatin
International Nuclear Information System (INIS)
Teif, Vladimir B; Rippe, Karsten
2010-01-01
Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibria measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical-mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.
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Using A Model-Based Systems Engineering Approach For Exploration Medical System Development
Hanson, A.; Mindock, J.; McGuire, K.; Reilly, J.; Cerro, J.; Othon, W.; Rubin, D.; Urbina, M.; Canga, M.
2017-01-01
NASA's Human Research Program's Exploration Medical Capabilities (ExMC) element is defining the medical system needs for exploration class missions. ExMC's Systems Engineering (SE) team will play a critical role in successful design and implementation of the medical system into exploration vehicles. The team's mission is to "Define, develop, validate, and manage the technical system design needed to implement exploration medical capabilities for Mars and test the design in a progression of proving grounds." Development of the medical system is being conducted in parallel with exploration mission architecture and vehicle design development. Successful implementation of the medical system in this environment will require a robust systems engineering approach to enable technical communication across communities to create a common mental model of the emergent engineering and medical systems. Model-Based Systems Engineering (MBSE) improves shared understanding of system needs and constraints between stakeholders and offers a common language for analysis. The ExMC SE team is using MBSE techniques to define operational needs, decompose requirements and architecture, and identify medical capabilities needed to support human exploration. Systems Modeling Language (SysML) is the specific language the SE team is utilizing, within an MBSE approach, to model the medical system functional needs, requirements, and architecture. Modeling methods are being developed through the practice of MBSE within the team, and tools are being selected to support meta-data exchange as integration points to other system models are identified. Use of MBSE is supporting the development of relationships across disciplines and NASA Centers to build trust and enable teamwork, enhance visibility of team goals, foster a culture of unbiased learning and serving, and be responsive to customer needs. The MBSE approach to medical system design offers a paradigm shift toward greater integration between
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From medical imaging data to 3D printed anatomical models.
Directory of Open Access Journals (Sweden)
Thore M Bücking
Full Text Available Anatomical models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D printers have made it possible to create cost-efficient patient-specific models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT to 3D printed physical models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an anatomical model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.
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Computer modelling of statistical properties of SASE FEL radiation
International Nuclear Information System (INIS)
Saldin, E. L.; Schneidmiller, E. A.; Yurkov, M. V.
1997-01-01
The paper describes an approach to computer modelling of statistical properties of the radiation from self amplified spontaneous emission free electron laser (SASE FEL). The present approach allows one to calculate the following statistical properties of the SASE FEL radiation: time and spectral field correlation functions, distribution of the fluctuations of the instantaneous radiation power, distribution of the energy in the electron bunch, distribution of the radiation energy after monochromator installed at the FEL amplifier exit and the radiation spectrum. All numerical results presented in the paper have been calculated for the 70 nm SASE FEL at the TESLA Test Facility being under construction at DESY
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Image sequence analysis in nuclear medicine: (1) Parametric imaging using statistical modelling
International Nuclear Information System (INIS)
Liehn, J.C.; Hannequin, P.; Valeyre, J.
1989-01-01
This is a review of parametric imaging methods on Nuclear Medicine. A Parametric Image is an image in which each pixel value is a function of the value of the same pixel of an image sequence. The Local Model Method is the fitting of each pixel time activity curve by a model which parameter values form the Parametric Images. The Global Model Method is the modelling of the changes between two images. It is applied to image comparison. For both methods, the different models, the identification criterion, the optimization methods and the statistical properties of the images are discussed. The analysis of one or more Parametric Images is performed using 1D or 2D histograms. The statistically significant Parametric Images, (Images of significant Variances, Amplitudes and Differences) are also proposed [fr
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Experimental investigation of statistical models describing distribution of counts
International Nuclear Information System (INIS)
Salma, I.; Zemplen-Papp, E.
1992-01-01
The binomial, Poisson and modified Poisson models which are used for describing the statistical nature of the distribution of counts are compared theoretically, and conclusions for application are considered. The validity of the Poisson and the modified Poisson statistical distribution for observing k events in a short time interval is investigated experimentally for various measuring times. The experiments to measure the influence of the significant radioactive decay were performed with 89 Y m (T 1/2 =16.06 s), using a multichannel analyser (4096 channels) in the multiscaling mode. According to the results, Poisson statistics describe the counting experiment for short measuring times (up to T=0.5T 1/2 ) and its application is recommended. However, analysis of the data demonstrated, with confidence, that for long measurements (T≥T 1/2 ) Poisson distribution is not valid and the modified Poisson function is preferable. The practical implications in calculating uncertainties and in optimizing the measuring time are discussed. Differences between the standard deviations evaluated on the basis of the Poisson and binomial models are especially significant for experiments with long measuring time (T/T 1/2 ≥2) and/or large detection efficiency (ε>0.30). Optimization of the measuring time for paired observations yields the same solution for either the binomial or the Poisson distribution. (orig.)
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Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*
Castruccio, Stefano
2014-03-01
The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.
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Directory of Open Access Journals (Sweden)
F. J. Barbero
2006-09-01
Full Text Available In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain, a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA, a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%. The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%. This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.
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A statistical skull geometry model for children 0-3 years old.
Directory of Open Access Journals (Sweden)
Zhigang Li
Full Text Available Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO. To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.
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A statistical skull geometry model for children 0-3 years old.
Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen
2015-01-01
Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.
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Automated parameter estimation for biological models using Bayesian statistical model checking.
Hussain, Faraz; Langmead, Christopher J; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K
2015-01-01
Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.
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A Statistical Graphical Model of the California Reservoir System
Taeb, A.; Reager, J. T.; Turmon, M.; Chandrasekaran, V.
2017-11-01
The recent California drought has highlighted the potential vulnerability of the state's water management infrastructure to multiyear dry intervals. Due to the high complexity of the network, dynamic storage changes in California reservoirs on a state-wide scale have previously been difficult to model using either traditional statistical or physical approaches. Indeed, although there is a significant line of research on exploring models for single (or a small number of) reservoirs, these approaches are not amenable to a system-wide modeling of the California reservoir network due to the spatial and hydrological heterogeneities of the system. In this work, we develop a state-wide statistical graphical model to characterize the dependencies among a collection of 55 major California reservoirs across the state; this model is defined with respect to a graph in which the nodes index reservoirs and the edges specify the relationships or dependencies between reservoirs. We obtain and validate this model in a data-driven manner based on reservoir volumes over the period 2003-2016. A key feature of our framework is a quantification of the effects of external phenomena that influence the entire reservoir network. We further characterize the degree to which physical factors (e.g., state-wide Palmer Drought Severity Index (PDSI), average temperature, snow pack) and economic factors (e.g., consumer price index, number of agricultural workers) explain these external influences. As a consequence of this analysis, we obtain a system-wide health diagnosis of the reservoir network as a function of PDSI.
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Image analysis and modeling in medical image computing. Recent developments and advances.
Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T
2012-01-01
Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body
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Hiemstra, Djoerd; de Jong, Franciska M.G.
2001-01-01
Traditionally, natural language processing techniques for information retrieval have always been studied outside the framework of formal models of information retrieval. In this article, we introduce a new formal model of information retrieval based on the application of statistical language models.
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ARSENIC CONTAMINATION IN GROUNDWATER: A STATISTICAL MODELING
Directory of Open Access Journals (Sweden)
Palas Roy
2013-01-01
Full Text Available High arsenic in natural groundwater in most of the tubewells of the Purbasthali- Block II area of Burdwan district (W.B, India has recently been focused as a serious environmental concern. This paper is intending to illustrate the statistical modeling of the arsenic contaminated groundwater to identify the interrelation of that arsenic contain with other participating groundwater parameters so that the arsenic contamination level can easily be predicted by analyzing only such parameters. Multivariate data analysis was done with the collected groundwater samples from the 132 tubewells of this contaminated region shows that three variable parameters are significantly related with the arsenic. Based on these relationships, a multiple linear regression model has been developed that estimated the arsenic contamination by measuring such three predictor parameters of the groundwater variables in the contaminated aquifer. This model could also be a suggestive tool while designing the arsenic removal scheme for any affected groundwater.
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[New model of doctor-nurse communication based on electronic medical advice platform].
Cao, Yang; Ding, Aimin; Wang, Yan
2012-01-01
This article introduces a new model of the communication between doctors and nurses, with the aid of the electronic medical advice platform. This model has achieved good results in improving doctor and nurse's co-working efficiency, treating patients safely, preventing medical accidents, reducing medical errors and so on.
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Schedulability of Herschel revisited using statistical model checking
DEFF Research Database (Denmark)
David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel
2015-01-01
-approximation technique. We can safely conclude that the system is schedulable for varying values of BCET. For the cases where deadlines are violated, we use polyhedra to try to confirm the witnesses. Our alternative method to confirm non-schedulability uses statistical model-checking (SMC) to generate counter...... and blocking times of tasks. Consequently, the method may falsely declare deadline violations that will never occur during execution. This paper is a continuation of previous work of the authors in applying extended timed automata model checking (using the tool UPPAAL) to obtain more exact schedulability...... analysis, here in the presence of non-deterministic computation times of tasks given by intervals [BCET,WCET]. Computation intervals with preemptive schedulers make the schedulability analysis of the resulting task model undecidable. Our contribution is to propose a combination of model checking techniques...
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The benefits of 3D modelling and animation in medical teaching.
Vernon, Tim; Peckham, Daniel
2002-12-01
Three-dimensional models created using materials such as wax, bronze and ivory, have been used in the teaching of medicine for many centuries. Today, computer technology allows medical illustrators to create virtual three-dimensional medical models. This paper considers the benefits of using still and animated output from computer-generated models in the teaching of medicine, and examines how three-dimensional models are made.
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[Chinese constitution research and the practice of 4P medical model].
Wang, Ji; Wang, Qi
2012-05-01
The aim of modern medicine is transforming from disease to health. Thus the medical model of 4P was proposed in recent years. 4P includes preventive, predictive, personalized, and participatory medical model. In constitution theory of Chinese medicine, there are three main ideas. The first one is: constitutions can be divided to nine types in the Chinese population. Prevention and treatment of disease can be divided according to the constitutional type. This reflects personalized or individualized of 4P. The second one is: certain constitution is correlated to certain disease. So constitution differentiation can be used to predict the occurrence of any kind disease. The third one is: Disease can be prevented through regulating correlated constitutions. And during the course of constitution differentiation, the object of service or patients can participate in the whole course. In summary, the research of Chinese medical constitution embodies the application and practice of 4P medical model. And it provided reference for studying and developing other subjects under the present medical model.
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Understanding advanced statistical methods
Westfall, Peter
2013-01-01
Introduction: Probability, Statistics, and ScienceReality, Nature, Science, and ModelsStatistical Processes: Nature, Design and Measurement, and DataModelsDeterministic ModelsVariabilityParametersPurely Probabilistic Statistical ModelsStatistical Models with Both Deterministic and Probabilistic ComponentsStatistical InferenceGood and Bad ModelsUses of Probability ModelsRandom Variables and Their Probability DistributionsIntroductionTypes of Random Variables: Nominal, Ordinal, and ContinuousDiscrete Probability Distribution FunctionsContinuous Probability Distribution FunctionsSome Calculus-Derivatives and Least SquaresMore Calculus-Integrals and Cumulative Distribution FunctionsProbability Calculation and SimulationIntroductionAnalytic Calculations, Discrete and Continuous CasesSimulation-Based ApproximationGenerating Random NumbersIdentifying DistributionsIntroductionIdentifying Distributions from Theory AloneUsing Data: Estimating Distributions via the HistogramQuantiles: Theoretical and Data-Based Estimate...
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Modelling Cooperative Work at a Medical Department
DEFF Research Database (Denmark)
Christensen, Lars Rune; Hildebrandt, Thomas
2017-01-01
Based on ethnographic fieldwork, and the modelling of work processes at a medical department, this paper considers some of the opportunities and challenges involved in working with models in a complex work setting. The paper introduces a flexible modelling tool to CSCW, called the DCR Portal......, and considers how it may be used to model complex work settings collaboratively. Further, the paper discusses how models created with the DCR portal may potentially play a key role in making a cooperative work ensemble appreciate, discuss and coordinate key interdependencies inherent to their cooperative work...
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Statistical pairwise interaction model of stock market
Bury, Thomas
2013-03-01
Financial markets are a classical example of complex systems as they are compound by many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price of restrictive assumptions on the market dynamics or they are agent-based models with rules designed in order to recover some empirical behaviors. Here we show that the pairwise model is actually a statistically consistent model with the observed first and second moments of the stocks orientation without making such restrictive assumptions. This is done with an approach only based on empirical data of price returns. Our data analysis of six major indices suggests that the actual interaction structure may be thought as an Ising model on a complex network with interaction strengths scaling as the inverse of the system size. This has potentially important implications since many properties of such a model are already known and some techniques of the spin glass theory can be straightforwardly applied. Typical behaviors, as multiple equilibria or metastable states, different characteristic time scales, spatial patterns, order-disorder, could find an explanation in this picture.
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GIGMF - A statistical model program
International Nuclear Information System (INIS)
Vladuca, G.; Deberth, C.
1978-01-01
The program GIGMF computes the differential and integrated statistical model cross sections for the reactions proceeding through a compound nuclear stage. The computational method is based on the Hauser-Feshbach-Wolfenstein theory, modified to include the modern version of Tepel et al. Although the program was written for a PDP-15 computer, with 16K high speed memory, many reaction channels can be taken into account with the following restrictions: the pro ectile spin must be less than 2, the maximum spin momenta of the compound nucleus can not be greater than 10. These restrictions are due solely to the storage allotments and may be easily relaxed. The energy of the impinging particle, the target and projectile masses, the spin and paritjes of the projectile, target, emergent and residual nuclei the maximum orbital momentum and transmission coefficients for each reaction channel are the input parameters of the program. (author)
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Variability aware compact model characterization for statistical circuit design optimization
Qiao, Ying; Qian, Kun; Spanos, Costas J.
2012-03-01
Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.
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Ontological Model-Based Transparent Access To Information In A Medical Multi-Agent System
Directory of Open Access Journals (Sweden)
Felicia GÎZĂ-BELCIUG
2012-01-01
Full Text Available Getting the full electronic medical record of a patient is an important step in providing a quality medical service. But the degree of heterogeneity of data from health unit informational systems is very high, because each unit can have a different model for storing patients’ medical data. In order to achieve the interoperability and integration of data from various medical units that store partial patient medical information, this paper proposes a multi-agent systems and ontology based approach. Therefore, we present an ontological model for describing the particular structure of the data integration process. The system is to be used for centralizing the information from a patient’s partial medical records. The main advantage of the proposed model is the low ratio between the complexity of the model and the amount of information that can be retrieved in order to generate the complete medical history of a patient.
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Three-Dimensional Assembly Tolerance Analysis Based on the Jacobian-Torsor Statistical Model
Directory of Open Access Journals (Sweden)
Peng Heping
2017-01-01
Full Text Available The unified Jacobian-Torsor model has been developed for deterministic (worst case tolerance analysis. This paper presents a comprehensive model for performing statistical tolerance analysis by integrating the unified Jacobian-Torsor model and Monte Carlo simulation. In this model, an assembly is sub-divided into surfaces, the Small Displacements Torsor (SDT parameters are used to express the relative position between any two surfaces of the assembly. Then, 3D dimension-chain can be created by using a surface graph of the assembly and the unified Jacobian-Torsor model is developed based on the effect of each functional element on the whole functional requirements of products. Finally, Monte Carlo simulation is implemented for the statistical tolerance analysis. A numerical example is given to demonstrate the capability of the proposed method in handling three-dimensional assembly tolerance analysis.
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Constructing an Urban Population Model for Medical Insurance Scheme Using Microsimulation Techniques
Directory of Open Access Journals (Sweden)
Linping Xiong
2012-01-01
Full Text Available China launched a pilot project of medical insurance reform in 79 cities in 2007 to cover urban nonworking residents. An urban population model was created in this paper for China’s medical insurance scheme using microsimulation model techniques. The model made it clear for the policy makers the population distributions of different groups of people, the potential urban residents entering the medical insurance scheme. The income trends of units of individuals and families were also obtained. These factors are essential in making the challenging policy decisions when considering to balance the long-term financial sustainability of the medical insurance scheme.
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Higher Referrals for Diabetes Education in a Medical Home Model of Care.
Manard, William T; Syberg, Kevin; Behera, Anit; Salas, Joanne; Schneider, F David; Armbrecht, Eric; Hooks-Anderson, Denise; Crannage, Erica; Scherrer, Jeffrey
2016-01-01
The medical home model has been gaining attention from the health care community as a strategy for improved outcomes for management of chronic disease, including diabetes. The purpose of this study was to compare referrals for diabetes education among patients receiving care from a medical home model versus a traditional practice. Data were obtained from a large, university-affiliated primary care patient data registry. All patients (age 18-96 years) with a diagnosis of prediabetes or diabetes and seen by a physician at least twice during 2011 to 2013 were selected for inclusion. Multivariate regression models measuring the association between medical home status and referral to diabetes education were computed before and after adjusting for covariates. A significantly (P patients in a medical home than without a medical home (23.9% vs 13.5%) received a referral for diabetes education. After adjusting for covariates, medical home patients were 2.7 times more likely to receive a referral for diabetes education (odds ratio, 2.70; 95% confidence interval, 1.69-4.35). Patients in a medical home model were more likely to receive referrals for diabetes education than patients in a standard university-affiliated family medicine practice. Future longitudinal designs that match characteristics of patients with a medical home with those of patients without one will provide strong evidence to determine whether referral to diabetes education is a result of the medical home model of care independent of confounding factors. © Copyright 2016 by the American Board of Family Medicine.
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Tyler, Ingrid V; Hau, Monica; Buxton, Jane A; Elliott, Lawrence J; Harvey, Bart J; Hockin, James C; Mowat, David L
2009-09-01
To understand the perceptions and attitudes of Canadian medical students toward their undergraduate medical public health curriculum and to identify student suggestions and priorities for curriculum change. Five focus groups of 11 or 12 medical students from all years of medical school were recruited at McMaster University Faculty of Health Sciences, Université de Sherbrooke Faculty of Medicine and Health Sciences, University of Toronto Faculty of Medicine, University of Manitoba Faculty of Medicine, and University of British Columbia Faculty of Medicine between February and April 2006. A professional facilitator was hired to conduct the focus groups using a unique, computer-based facilitation system. Questions in both the focus group and an accompanying survey sought to determine medical students' understanding and exposure to public health and how this impacted their attitudes and choices toward careers in the public health medical specialty of community medicine. The transcripts were independently reviewed and analyzed by each of the authors to identify themes. Four major themes related to students' desired curriculum change were identified: (1) poor educational experiences in public health courses, (2) lack of positive role models, especially exposure to community medicine specialists, (3) emphasis on statistics and epidemiology, and (4) negative attitudes toward public health topics. Students are disillusioned, disengaged, and disappointed with the public health curriculum currently being provided at the Canadian medical schools studied. Many medical students would prefer a public health curriculum that is more challenging and has more applied field experience and exposure to public health physician role models.
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Statistical Modeling of Large-Scale Signal Path Loss in Underwater Acoustic Networks
Directory of Open Access Journals (Sweden)
Manuel Perez Malumbres
2013-02-01
Full Text Available In an underwater acoustic channel, the propagation conditions are known to vary in time, causing the deviation of the received signal strength from the nominal value predicted by a deterministic propagation model. To facilitate a large-scale system design in such conditions (e.g., power allocation, we have developed a statistical propagation model in which the transmission loss is treated as a random variable. By applying repetitive computation to the acoustic field, using ray tracing for a set of varying environmental conditions (surface height, wave activity, small node displacements around nominal locations, etc., an ensemble of transmission losses is compiled and later used to infer the statistical model parameters. A reasonable agreement is found with log-normal distribution, whose mean obeys a log-distance increases, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. The statistical model is deemed useful for higher-level system planning, where simulation is needed to assess the performance of candidate network protocols under various resource allocation policies, i.e., to determine the transmit power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc..
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Directory of Open Access Journals (Sweden)
Nancy Dao
2018-05-01
Full Text Available Appointment-based medication synchronization (ABMS programs have been associated with increased adherence and persistence to chronic medications. Adherence to statin therapy, angiotensin-converting enzyme inhibitors (ACEIs, angiotensin receptor blockers (ARBs, and non-insulin antidiabetic medications (NIDM are used to determine a health plan’s Centers for Medicare and Medicaid Services (CMS Star Rating under a pay-for-performance model. The objective of this study was to evaluate the impact of implementing an ABMS program on overall pharmacy adherence measures for statins, ACEI/ARBs, and NIDM, as presented through the Electronic Quality Improvement Platform for Plans and Pharmacies (EQuIPP© platform. This retrospective, pre-post ABMS program study evaluated EQuIPP© generated adherence performance measures, represented as proportion of days covered (PDC, 6-months before and 6- and 12-months after the ABMS service for statin therapy, ACEIs/ARBs, and NIDM. All adherence measures showed statistically significant improvement in PDC percentage post ABMS implementation, except for NIDM percentage in 6-months post-ABMS service. This study shows that a comprehensive medication synchronization program can enhance adherence measures that are important to health plans to increase CMS Star Rating under a pay-for-performance model.
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Müller, M. F.; Thompson, S. E.
2016-02-01
The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.
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Two statistical approaches, weighted regression on time, discharge, and season and generalized additive models, have recently been used to evaluate water quality trends in estuaries. Both models have been used in similar contexts despite differences in statistical foundations and...
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Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System
Directory of Open Access Journals (Sweden)
Wuyang Cheng
2014-01-01
Full Text Available We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI and Hang Seng Index (HSI are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.
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Description of a practice model for pharmacist medication review in a general practice setting
DEFF Research Database (Denmark)
Brandt, Mette; Hallas, Jesper; Hansen, Trine Graabæk
2014-01-01
BACKGROUND: Practical descriptions of procedures used for pharmacists' medication reviews are sparse. OBJECTIVE: To describe a model for medication review by pharmacists tailored to a general practice setting. METHODS: A stepwise model is described. The model is based on data from the medical chart...... no indication (n=47, 23%). Most interventions were aimed at cardiovascular drugs. CONCLUSION: We have provided a detailed description of a practical approach to pharmacists' medication review in a GP setting. The model was tested and found to be usable, and to deliver a medication review with high acceptance...
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Statistical properties of three-dimensional two-fluid plasma model
Energy Technology Data Exchange (ETDEWEB)
Qaisrani, M. Hasnain [State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, WuHan, Hubei 430074 (China); Xia, ZhenWei [Department of Modern Physics, University of Science and Technology of China, Hefei 230026 (China); Zou, Dandan, E-mail: ddzou@hust.edu.cn [State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, WuHan, Hubei 430074 (China); School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023 (China)
2015-09-15
The nonlinear dynamics of incompressible non-dissipative two-fluid plasma model is investigated through classical Gibbs ensemble methods. Liouville's theorem of phase space for each wave number is proved, and the absolute equilibrium spectra for Galerkin truncated two-fluid model are calculated. In two-fluid theory, the equilibrium is built on the conservation of three quadratic invariants: the total energy and the self-helicities for ions and electrons fluid, respectively. The implications of statistic equilibrium spectra with arbitrary ratios of conserved invariants are discussed.
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Huser, Raphaë l; Opitz, Thomas; Thibaud, Emeric
2018-01-01
Extreme-value theory for stochastic processes has motivated the statistical use of max-stable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability
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Hadronic equation of state in the statistical bootstrap model and linear graph theory
International Nuclear Information System (INIS)
Fre, P.; Page, R.
1976-01-01
Taking a statistical mechanical point og view, the statistical bootstrap model is discussed and, from a critical analysis of the bootstrap volume comcept, it is reached a physical ipothesis, which leads immediately to the hadronic equation of state provided by the bootstrap integral equation. In this context also the connection between the statistical bootstrap and the linear graph theory approach to interacting gases is analyzed
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Oseloka Ezepue, Patrick; Ojo, Adegbola
2012-12-01
A challenging problem in some developing countries such as Nigeria is inadequate training of students in effective problem solving using the core concepts of their disciplines. Related to this is a disconnection between their learning and socio-economic development agenda of a country. These problems are more vivid in statistical education which is dominated by textbook examples and unbalanced assessment 'for' and 'of' learning within traditional curricula. The problems impede the achievement of socio-economic development objectives such as those stated in the Nigerian Vision 2020 blueprint and United Nations Millennium Development Goals. They also impoverish the ability of (statistics) graduates to creatively use their knowledge in relevant business and industry sectors, thereby exacerbating mass graduate unemployment in Nigeria and similar developing countries. This article uses a case study in statistical modelling to discuss the nature of innovations in statistics education vital to producing new kinds of graduates who can link their learning to national economic development goals, create wealth and alleviate poverty through (self) employment. Wider implications of the innovations for repositioning mathematical sciences education globally are explored in this article.
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Huffman and linear scanning methods with statistical language models.
Roark, Brian; Fried-Oken, Melanie; Gibbons, Chris
2015-03-01
Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning.
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A statistical model for radar images of agricultural scenes
Frost, V. S.; Shanmugan, K. S.; Holtzman, J. C.; Stiles, J. A.
1982-01-01
The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.
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Skinner, Carl G; Patel, Manish M; Thomas, Jerry D; Miller, Michael A
2011-01-01
Statistical methods are pervasive in medical research and general medical literature. Understanding general statistical concepts will enhance our ability to critically appraise the current literature and ultimately improve the delivery of patient care. This article intends to provide an overview of the common statistical methods relevant to medicine.
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Kleijnen, J.P.C.
1995-01-01
This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for
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Medical applications of model-based dynamic thermography
Nowakowski, Antoni; Kaczmarek, Mariusz; Ruminski, Jacek; Hryciuk, Marcin; Renkielska, Alicja; Grudzinski, Jacek; Siebert, Janusz; Jagielak, Dariusz; Rogowski, Jan; Roszak, Krzysztof; Stojek, Wojciech
2001-03-01
The proposal to use active thermography in medical diagnostics is promising in some applications concerning investigation of directly accessible parts of the human body. The combination of dynamic thermograms with thermal models of investigated structures gives attractive possibility to make internal structure reconstruction basing on different thermal properties of biological tissues. Measurements of temperature distribution synchronized with external light excitation allow registration of dynamic changes of local temperature dependent on heat exchange conditions. Preliminary results of active thermography applications in medicine are discussed. For skin and under- skin tissues an equivalent thermal model may be determined. For the assumed model its effective parameters may be reconstructed basing on the results of transient thermal processes. For known thermal diffusivity and conductivity of specific tissues the local thickness of a two or three layer structure may be calculated. Results of some medical cases as well as reference data of in vivo study on animals are presented. The method was also applied to evaluate the state of the human heart during the open chest cardio-surgical interventions. Reference studies of evoked heart infarct in pigs are referred, too. We see the proposed new in medical applications technique as a promising diagnostic tool. It is a fully non-invasive, clean, handy, fast and affordable method giving not only qualitative view of investigated surfaces but also an objective quantitative measurement result, accurate enough for many applications including fast screening of affected tissues.
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Comparison of stress among medical and not medical personnel in health care
Directory of Open Access Journals (Sweden)
Anita Mujakić
2014-09-01
Full Text Available Research Question (RQ: Does the opinion of the medical and non - medical staff varies on (1 the level of stress depending on seniority, (2 the staircase of stress in relation to education, and (3 how motivation affects the level of stress. Purpose: The purpose of the study is to determine whether in health care and higher education contribute to increased job stress. Also, we wanted to know whether they are more motivated workers are less exposed to occupational stress and / or employees with higher seniority also more exposed to stress. Method: We did quantitative research in public health institute. Overview of theoretical principles based on domestic and foreign professional literature. Articles and expert input was obtained in electronic databases ProQuest Online Information Service, Ebsco and SpringerLink. Statistical part of the study, we calculated the statistical program where we used the Mann - Whitney U- statistics and Wilcox W-statistics. Results: The results indicate that there is a statistical difference in understanding the importance of seniority and education on occupational stress among medical and non - medical sector. Employees in the medical sector more statistically argue that education and working life affect the career stress. We rejected our second hypothesis, which says that there is a difference between the two sectors regarding the impact of motivation on occupational stress. Both sectors they consider to be less motivated workers exposed to occupational stress. Organization: The survey can further highlight the risks that may be possible due to congestion and occupational stress. Society: positive influence on the social understanding of diversity obtained service of a single profession and thus routing problem in a disproportionate burden of healthcare workers. Healthcare professionals who work under less stress effectively and positively affect the quality of services rendered. Originality: This kind of research by
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Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
Directory of Open Access Journals (Sweden)
Yuze Wang
2018-04-01
Full Text Available A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.
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Statistical model for OCT image denoising
Li, Muxingzi
2017-08-01
Optical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.
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Beyond the standard of care: a new model to judge medical negligence.
Brenner, Lawrence H; Brenner, Alison Tytell; Awerbuch, Eric J; Horwitz, Daniel
2012-05-01
The term "standard of care" has been used in law and medicine to determine whether medical care is negligent. However, the precise meaning of this concept is often unclear for both medical and legal professionals. Our purposes are to (1) examine the limitations of using standard of care as a measure of negligence, (2) propose the use of the legal concepts of justification and excuse in developing a new model of examining medical conduct, and (3) outline the framework of this model. We applied the principles of tort liability set forth in the clinical and legal literature to describe the difficulty in applying standard of care in medical negligence cases. Using the concepts of justification and excuse, we propose a judicial model that may promote fair and just jury verdicts in medical negligence cases. Contrary to conventional understanding, medical negligence is not simply nonconformity to norms. Two additional concepts of legal liability, ie, justification and excuse, must also be considered to properly judge medical conduct. Medical conduct is justified when the benefits outweigh the risks; the law sanctions the conduct and encourages future conduct under similar circumstances. Excuse, on the other hand, relieves a doctor of legal liability under specific circumstances even though his/her conduct was not justified. Standard of care is an inaccurate measure of medical negligence because it is premised on the faulty notion of conformity to norms. An alternative judicial model to determine medical negligence would (1) eliminate standard of care in medical malpractice law, (2) reframe the court instruction to jurors, and (3) establish an ongoing consensus committee on orthopaedic principles of negligence.
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Statistical model for expected un supplied energy; Statistisk modell for forventet ILE
Energy Technology Data Exchange (ETDEWEB)
NONE
2005-07-01
Results from a statistical analysis of expected un supplied energy for Norwegian network companies are presented. The data are from the years 1996-2004. The estimation model includes several explanatory variables that together reflect the characteristics of the network, climatic aspects and other geographical conditions. The model has a high degree of accuracy when compared to the historical number of un supplied energy for about 90 percent of the network companies. But for 12 companies there are substantial, negative deviances that are not compatible with the available data. There is reason to believe that improved data for some types of variables can improve the accuracy of the model. In addition to establishing a norm for expected un supplied energy in the revenue estimations, the model can be used to reflect geographical constraints in NVEs (Norwegian Water and Energy directorate) efficiency analyses (ml)
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Hybrid perturbation methods based on statistical time series models
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
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Litchford, Ron J.; Jeng, San-Mou
1992-01-01
The performance of a recently introduced statistical transport model for turbulent particle dispersion is studied here for rigid particles injected into a round turbulent jet. Both uniform and isosceles triangle pdfs are used. The statistical sensitivity to parcel pdf shape is demonstrated.
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Probability of Detection (POD) as a statistical model for the validation of qualitative methods.
Wehling, Paul; LaBudde, Robert A; Brunelle, Sharon L; Nelson, Maria T
2011-01-01
A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.
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Average Nuclear properties based on statistical model
International Nuclear Information System (INIS)
El-Jaick, L.J.
1974-01-01
The rough properties of nuclei were investigated by statistical model, in systems with the same and different number of protons and neutrons, separately, considering the Coulomb energy in the last system. Some average nuclear properties were calculated based on the energy density of nuclear matter, from Weizsscker-Beth mass semiempiric formulae, generalized for compressible nuclei. In the study of a s surface energy coefficient, the great influence exercised by Coulomb energy and nuclear compressibility was verified. For a good adjust of beta stability lines and mass excess, the surface symmetry energy were established. (M.C.K.) [pt
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DEFF Research Database (Denmark)
A methodology is presented that combines modelling based on first principles and data based modelling into a modelling cycle that facilitates fast decision-making based on statistical methods. A strong feature of this methodology is that given a first principles model along with process data......, the corresponding modelling cycle model of the given system for a given purpose. A computer-aided tool, which integrates the elements of the modelling cycle, is also presented, and an example is given of modelling a fed-batch bioreactor....
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Can spatial statistical river temperature models be transferred between catchments?
Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.
2017-09-01
There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across
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Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
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SPD-based Logistics Management Model of Medical Consumables in Hospitals
LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei; YANG, Shanlin
2016-01-01
Background: With the rapid development of health services, the progress of medical science and technology, and the improvement of materials research, the consumption of medical consumables (MCs) in medical activities has increased in recent years. However, owing to the lack of effective management methods and the complexity of MCs, there are several management problems including MC waste, low management efficiency, high management difficulty, and frequent medical accidents. Therefore, there is urgent need for an effective logistics management model to handle these problems and challenges in hospitals. Methods: We reviewed books and scientific literature (by searching the articles published from 2010 to 2015 in Engineering Village database) to understand supply chain related theories and methods and performed field investigations in hospitals across many cities to determine the actual state of MC logistics management of hospitals in China. Results: We describe the definition, physical model, construction, and logistics operation processes of the supply, processing, and distribution (SPD) of MC logistics because of the traditional SPD model. With the establishment of a supply-procurement platform and a logistics lean management system, we applied the model to the MC logistics management of Anhui Provincial Hospital with good effects. Conclusion: The SPD model plays a critical role in optimizing the logistics procedures of MCs, improving the management efficiency of logistics, and reducing the costs of logistics of hospitals in China. PMID:27957435
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SPD-based Logistics Management Model of Medical Consumables in Hospitals.
Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei; Yang, Shanlin
2016-10-01
With the rapid development of health services, the progress of medical science and technology, and the improvement of materials research, the consumption of medical consumables (MCs) in medical activities has increased in recent years. However, owing to the lack of effective management methods and the complexity of MCs, there are several management problems including MC waste, low management efficiency, high management difficulty, and frequent medical accidents. Therefore, there is urgent need for an effective logistics management model to handle these problems and challenges in hospitals. We reviewed books and scientific literature (by searching the articles published from 2010 to 2015 in Engineering Village database) to understand supply chain related theories and methods and performed field investigations in hospitals across many cities to determine the actual state of MC logistics management of hospitals in China. We describe the definition, physical model, construction, and logistics operation processes of the supply, processing, and distribution (SPD) of MC logistics because of the traditional SPD model. With the establishment of a supply-procurement platform and a logistics lean management system, we applied the model to the MC logistics management of Anhui Provincial Hospital with good effects. The SPD model plays a critical role in optimizing the logistics procedures of MCs, improving the management efficiency of logistics, and reducing the costs of logistics of hospitals in China.
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Prior knowledge regularization in statistical medical image tasks
DEFF Research Database (Denmark)
Crimi, Alessandro; Sporring, Jon; de Bruijne, Marleen
2009-01-01
The estimation of the covariance matrix is a pivotal step inseveral statistical tasks. In particular, the estimation becomes challeng-ing for high dimensional representations of data when few samples areavailable. Using the standard Maximum Likelihood estimation (MLE)when the number of samples ar...
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Statistical models for thermal ageing of steel materials in nuclear power plants
International Nuclear Information System (INIS)
Persoz, M.
1996-01-01
Some category of steel materials in nuclear power plants may be subjected to thermal ageing, whose extent depends on the steel chemical composition and the ageing parameters, i.e. temperature and duration. This ageing affects the 'impact strength' of the materials, which is a mechanical property. In order to assess the residual lifetime of these components, a probabilistic study has been launched, which takes into account the scatter over the input parameters of the mechanical model. Predictive formulae for estimating the impact strength of aged materials are important input data of the model. A data base has been created with impact strength results obtained from an ageing program in laboratory and statistical treatments have been undertaken. Two kinds of model have been developed, with non linear regression methods (PROC NLIN, available in SAS/STAT). The first one, using a hyperbolic tangent function, is partly based on physical considerations, and the second one, of an exponential type, is purely statistically built. The difficulties consist in selecting the significant parameters and attributing initial values to the coefficients, which is a requirement of the NLIN procedure. This global statistical analysis has led to general models that are unction of the chemical variables and the ageing parameters. These models are as precise (if not more) as local models that had been developed earlier for some specific values of ageing temperature and ageing duration. This paper describes the data and the methodology used to build the models and analyses the results given by the SAS system. (author)
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Statistical data analysis using SAS intermediate statistical methods
Marasinghe, Mervyn G
2018-01-01
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitab...
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Tornadoes and related damage costs: statistical modelling with a semi-Markov approach
Directory of Open Access Journals (Sweden)
Guglielmo D’Amico
2016-09-01
Full Text Available We propose a statistical approach to modelling for predicting and simulating occurrences of tornadoes and accumulated cost distributions over a time interval. This is achieved by modelling the tornado intensity, measured with the Fujita scale, as a stochastic process. Since the Fujita scale divides tornado intensity into six states, it is possible to model the tornado intensity by using Markov and semi-Markov models. We demonstrate that the semi-Markov approach is able to reproduce the duration effect that is detected in tornado occurrence. The superiority of the semi-Markov model as compared to the Markov chain model is also affirmed by means of a statistical test of hypothesis. As an application, we compute the expected value and the variance of the costs generated by the tornadoes over a given time interval in a given area. The paper contributes to the literature by demonstrating that semi-Markov models represent an effective tool for physical analysis of tornadoes as well as for the estimation of the economic damages to human things.
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Cressie, Noel; Calder, Catherine A; Clark, James S; Ver Hoef, Jay M; Wikle, Christopher K
2009-04-01
Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.
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Frank, Laurence Emmanuelle
2006-01-01
Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor
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Function-based payment model for inpatient medical rehabilitation: an evaluation.
Sutton, J P; DeJong, G; Wilkerson, D
1996-07-01
To describe the components of a function-based prospective payment model for inpatient medical rehabilitation that parallels diagnosis-related groups (DRGs), to evaluate this model in relation to stakeholder objectives, and to detail the components of a quality of care incentive program that, when combined with this payment model, creates an incentive for provides to maximize functional outcomes. This article describes a conceptual model, involving no data collection or data synthesis. The basic payment model described parallels DRGs. Information on the potential impact of this model on medical rehabilitation is gleaned from the literature evaluating the impact of DRGs. The conceptual model described is evaluated against the results of a Delphi Survey of rehabilitation providers, consumers, policymakers, and researchers previously conducted by members of the research team. The major shortcoming of a function-based prospective payment model for inpatient medical rehabilitation is that it contains no inherent incentive to maximize functional outcomes. Linkage of reimbursement to outcomes, however, by withholding a fixed proportion of the standard FRG payment amount, placing that amount in a "quality of care" pool, and distributing that pool annually among providers whose predesignated, facility-level, case-mix-adjusted outcomes are attained, may be one strategy for maximizing outcome goals.
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Securing wide appreciation of health statistics.
PYRRAIT A M DO, A; AUBENQUE, M J; BENJAMIN, B; DE GROOT, M J; KOHN, R
1954-01-01
All the authors are agreed on the need for a certain publicizing of health statistics, but do Amaral Pyrrait points out that the medical profession prefers to convince itself rather than to be convinced. While there is great utility in articles and reviews in the professional press (especially for paramedical personnel) Aubenque, de Groot, and Kohn show how appreciation can effectively be secured by making statistics more easily understandable to the non-expert by, for instance, including readable commentaries in official publications, simplifying charts and tables, and preparing simple manuals on statistical methods. Aubenque and Kohn also stress the importance of linking health statistics to other economic and social information. Benjamin suggests that the principles of market research could to advantage be applied to health statistics to determine the precise needs of the "consumers". At the same time, Aubenque points out that the value of the ultimate results must be clear to those who provide the data; for this, Kohn suggests that the enumerators must know exactly what is wanted and why.There is general agreement that some explanation of statistical methods and their uses should be given in the curricula of medical schools and that lectures and postgraduate courses should be arranged for practising physicians.
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α -induced reactions on 115In: Cross section measurements and statistical model analysis
Kiss, G. G.; Szücs, T.; Mohr, P.; Török, Zs.; Huszánk, R.; Gyürky, Gy.; Fülöp, Zs.
2018-05-01
Background: α -nucleus optical potentials are basic ingredients of statistical model calculations used in nucleosynthesis simulations. While the nucleon+nucleus optical potential is fairly well known, for the α +nucleus optical potential several different parameter sets exist and large deviations, reaching sometimes even an order of magnitude, are found between the cross section predictions calculated using different parameter sets. Purpose: A measurement of the radiative α -capture and the α -induced reaction cross sections on the nucleus 115In at low energies allows a stringent test of statistical model predictions. Since experimental data are scarce in this mass region, this measurement can be an important input to test the global applicability of α +nucleus optical model potentials and further ingredients of the statistical model. Methods: The reaction cross sections were measured by means of the activation method. The produced activities were determined by off-line detection of the γ rays and characteristic x rays emitted during the electron capture decay of the produced Sb isotopes. The 115In(α ,γ )119Sb and 115In(α ,n )Sb118m reaction cross sections were measured between Ec .m .=8.83 and 15.58 MeV, and the 115In(α ,n )Sb118g reaction was studied between Ec .m .=11.10 and 15.58 MeV. The theoretical analysis was performed within the statistical model. Results: The simultaneous measurement of the (α ,γ ) and (α ,n ) cross sections allowed us to determine a best-fit combination of all parameters for the statistical model. The α +nucleus optical potential is identified as the most important input for the statistical model. The best fit is obtained for the new Atomki-V1 potential, and good reproduction of the experimental data is also achieved for the first version of the Demetriou potentials and the simple McFadden-Satchler potential. The nucleon optical potential, the γ -ray strength function, and the level density parametrization are also