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.
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
Lossloss encoding of medical images: hybrid modification of statistical modelling-based conception
Przelaskowski, Artur
2001-10-01
Methods of lossless compression of medical image data are considered. Selected class of efficient algorithms have been constructed, examined, and optimized to conclude the most useful tools for medical image archiving and transmission. Image data scanning, 2D context-based prediction and interpolation, and statistical models of entropy coder have been optimized to compress effectively ultrasound (US), magnetic resonance (MR), and computed tomography (CT) images. The SSM technique of suitable data decomposing scanning method followed by probabilistic modeling of the context in arithmetic encoding have occurred the most useful in our experiments. Context order, shape, and alphabet have been fitted to local data characteristics to decrease image data correlation and dilution of statistical model. Average bit rate value over test images is equal to 2.53 bpp for SSM coder and significantly overcomes 2.92 bpp of CALIC bit rate. Moreover, optimization of lossless wavelet coder by thinking of efficient subband decomposition schemes, and integer-to-integer transforms is reported. Efficient hybrid coding method (SHEC) as a complete tool for medical image archiving and transmission is proposed. SHEC develops SSM by including CALIC-like coder to compress the highest quality images and JPEG2000 wavelet coder for progressive delivering of high and middle quality images in telemedicine systems.
Onisko, Agnieszka; Druzdzel, Marek J.; Austin, R. Marshall
2016-01-01
Background: 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. Aim: 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. Materials and Methods: 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. Results: 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. Conclusion: 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. PMID:28163973
Applied medical statistics using SAS
Der, Geoff
2012-01-01
""Each chapter in the book is well laid out, contains examples with SAS code, and ends with a concise summary. The chapters in the book contain the right level of information to use SAS to apply different statistical methods. … a good overview of how to apply in SAS 9.3 the many possible statistical analysis methods.""-Caroline Kennedy, Takeda Development Centre Europe Ltd., Statistical Methods for Medical Research, 2015""… a well-organized and thorough exploration of broad coverage in medical statistics. The book is an excellent reference of statistical methods
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.
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
Peculiarities of Teaching Medical Informatics and Statistics
Glushkov, Sergey
2017-01-01
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…
Interpretation and uses of medical statistics
Daly, Leslie
2008-01-01
In 1969 the first edition of this book introduced the concepts of statistics and their medical application to readers with no formal training in this area. While retaining this basic aim, the authors have expanded the coverage in each subsequent edition to keep pace with the increasing use and sophistication of statistics in medical research. This fifth edition has undergone major restructuring, with some sections completely rewritten; it is now more logically organized and more user friendly (with the addition of 'summary boxes' throughout the text). It incorporates new statistical techniq
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. Th...... 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....
Modeling cosmic void statistics
Hamaus, Nico; Sutter, P. M.; Wandelt, Benjamin D.
2016-10-01
Understanding the internal structure and spatial distribution of cosmic voids is crucial when considering them as probes of cosmology. We present recent advances in modeling void density- and velocity-profiles in real space, as well as void two-point statistics in redshift space, by examining voids identified via the watershed transform in state-of-the-art ΛCDM n-body simulations and mock galaxy catalogs. The simple and universal characteristics that emerge from these statistics indicate the self-similarity of large-scale structure and suggest cosmic voids to be among the most pristine objects to consider for future studies on the nature of dark energy, dark matter and modified gravity.
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.
Algebraic Statistics for Network Models
2014-02-19
AFRL-OSR-VA-TR-2014-0070 (DARPA) Algebraic Statistics for Network Models SONJA PETROVIC PENNSYLVANIA STATE UNIVERSITY 02/19/2014 Final Report...DARPA GRAPHS Phase I Algebraic Statistics for Network Models FA9550-12-1-0392 Sonja Petrović petrovic@psu.edu1 Department of Statistics Pennsylvania...Department of Statistics, Heinz College , Machine Learning Department, Cylab Carnegie Mellon University 1. Abstract This project focused on the family of
Statistical Model for Content Extraction
DEFF Research Database (Denmark)
2011-01-01
We present a statistical model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features to predict significance of the node towards overall content...
Methods of statistical model estimation
Hilbe, Joseph
2013-01-01
Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. Th
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
LP Approach to Statistical Modeling
Mukhopadhyay, Subhadeep; Parzen, Emanuel
2014-01-01
We present an approach to statistical data modeling and exploratory data analysis called `LP Statistical Data Science.' It aims to generalize and unify traditional and novel statistical measures, methods, and exploratory tools. This article outlines fundamental concepts along with real-data examples to illustrate how the `LP Statistical Algorithm' can systematically tackle different varieties of data types, data patterns, and data structures under a coherent theoretical framework. A fundament...
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....
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.
Foundational Issues in Statistical Modeling: Statistical Model Specification and Validation
Directory of Open Access Journals (Sweden)
Aris Spanos
2011-01-01
Full Text Available Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven approach, and the Akaike-type model selection procedures, arguing that they often lead to unreliable inferences. This is primarily due to the fact that goodness-of-fit/prediction measures and other substantive and pragmatic criteria are of questionable value when the estimated model is statistically misspecified. Foisting one's favorite model on the data often yields estimated models which are both statistically and substantively misspecified, but one has no way to delineate between the two sources of error and apportion blame. The paper argues that the error statistical approach can address this Duhemian ambiguity by distinguishing between statistical and substantive premises and viewing empirical modeling in a piecemeal way with a view to delineate the various issues more effectively. It is also argued that Hendry's general to specific procedures does a much better job in model selection than the theory-driven and the Akaike-type procedures primary because of its error statistical underpinnings.
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...
Statistical simulation of variations in medical characteristic during heliogeophysical disturbances
Sergeenko, N. P.
2016-12-01
Heliobiological studies, although recently very active, have failed to provide a full understanding of the Sun-biosphere interaction mechanisms. This is because the forecasts of bio tropic effects of the active Sun, like geophysical forecasts, are mostly determined by empirical patterns and are of a probability character. To obtain probability predictions, an adequate statistical model should be prepared. This is what is treated in this work. The time series of data from medical studies have undergone the trend treatment. The samples were sorted by the state of heliogeophysical conditions, and statistical distributions and invariants have been calculated down to the forth order. The analysis has shown that during heliogeomagnetic disturbances the obtained distributions differ from the Gaussian law. Therefore, an attempt at a statistical description of medical characteristics was made on the basis of the Poisson model. For our samples, the characteristic function of the exponential kind was chosen under the assumption that time series are the superposition of some, determined or random, process. Through the Fourier transform, the characteristic function was transformed into a nonholomorphic expressiveasymmetrical function of the probability density. The statistical distributions, calculated for the studied samples over perturbation periods, were compared with the obtained model function of distribution. The probability of coincidence for a posteriori distributions by the criterion χ2 is P 0.7-0.9. Our analysis has allowed a conclusion about the applicability of a model based on the Poisson random process for statistical description and appraisals of the probability of changes in the number of vascular bouts and strokes during heliogeophysical perturbations.
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.
Sensometrics: Thurstonian and Statistical Models
DEFF Research Database (Denmark)
Christensen, Rune Haubo Bojesen
of human senses. Thurstonian models provide a stochastic model for the data-generating mechanism through a psychophysical model for the cognitive processes and in addition provides an independent measure for quantification of sensory differences. In the interest of cost-reduction and health...... of generalized linear mixed models, cumulative link models and cumulative link mixed models. The relation between the Wald, likelihood and score statistics is expanded upon using the shape of the (profile) likelihood function as common reference....
A Statistical Programme Assignment Model
DEFF Research Database (Denmark)
Rosholm, Michael; Staghøj, Jonas; Svarer, Michael
assignment mechanism, which is based on the discretionary choice of case workers. This is done in a duration model context, using the timing-of-events framework to identify causal effects. We compare different assignment mechanisms, and the results suggest that a significant reduction in the average...... 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....
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...
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...... assignment mechanism, which is based on the discretionary choice of case workers. This is done in a duration model context, using the timing-of-events framework to identify causal effects. We compare different assignment mechanisms, and the results suggest that a significant reduction in the average...... 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....
Image quantization: statistics and modeling
Whiting, Bruce R.; Muka, Edward
1998-07-01
A method for analyzing the effects of quantization, developed for temporal one-dimensional signals, is extended to two- dimensional radiographic images. By calculating the probability density function for the second order statistics (the differences between nearest neighbor pixels) and utilizing its Fourier transform (the characteristic function), the effect of quantization on image statistics can be studied by the use of standard communication theory. The approach is demonstrated by characterizing the noise properties of a storage phosphor computed radiography system and the image statistics of a simple radiographic object (cylinder) and by comparing the model to experimental measurements. The role of quantization noise and the onset of contouring in image degradation are explained.
Statistical modeling of program performance
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2014-01-01
Full Text Available A task of evaluation of program performance often occurs in the process of design of computer systems or during iterative compilation. A traditional way to solve this problem is emulation of program execution on the target system. A modern alternative approach to evaluation of program performance is based on statistical modeling of program performance on a computer under investigation. This statistical method of modeling program performance called Velocitas was introduced in this work. The method and its implementation in the Adaptor framework were presented. Investigation of the method's effectiveness showed high adequacy of program performance prediction.
Statistical analysis of medical data using SAS
Der, Geoff
2005-01-01
An Introduction to SASDescribing and Summarizing DataBasic InferenceScatterplots Correlation: Simple Regression and SmoothingAnalysis of Variance and CovarianceMultiple RegressionLogistic RegressionThe Generalized Linear ModelGeneralized Additive ModelsNonlinear Regression ModelsThe Analysis of Longitudinal Data IThe Analysis of Longitudinal Data II: Models for Normal Response VariablesThe Analysis of Longitudinal Data III: Non-Normal ResponseSurvival AnalysisAnalysis Multivariate Date: Principal Components and Cluster AnalysisReferences
Statistics teaching in medical school: opinions of practising doctors.
Miles, Susan; Price, Gill M; Swift, Louise; Shepstone, Lee; Leinster, Sam J
2010-11-04
The General Medical Council expects UK medical graduates to gain some statistical knowledge during their undergraduate education; but provides no specific guidance as to amount, content or teaching method. Published work on statistics teaching for medical undergraduates has been dominated by medical statisticians, with little input from the doctors who will actually be using this knowledge and these skills after graduation. Furthermore, doctor's statistical training needs may have changed due to advances in information technology and the increasing importance of evidence-based medicine. Thus there exists a need to investigate the views of practising medical doctors as to the statistical training required for undergraduate medical students, based on their own use of these skills in daily practice. A questionnaire was designed to investigate doctors' views about undergraduate training in statistics and the need for these skills in daily practice, with a view to informing future teaching. The questionnaire was emailed to all clinicians with a link to the University of East Anglia Medical School. Open ended questions were included to elicit doctors' opinions about both their own undergraduate training in statistics and recommendations for the training of current medical students. Content analysis was performed by two of the authors to systematically categorize and describe all the responses provided by participants. 130 doctors responded, including both hospital consultants and general practitioners. The findings indicated that most had not recognised the value of their undergraduate teaching in statistics and probability at the time, but had subsequently found the skills relevant to their career. Suggestions for improving undergraduate teaching in these areas included referring to actual research and ensuring relevance to, and integration with, clinical practice. Grounding the teaching of statistics in the context of real research studies and including examples of
Statistics teaching in medical school: Opinions of practising doctors
Directory of Open Access Journals (Sweden)
Shepstone Lee
2010-11-01
Full Text Available Abstract Background The General Medical Council expects UK medical graduates to gain some statistical knowledge during their undergraduate education; but provides no specific guidance as to amount, content or teaching method. Published work on statistics teaching for medical undergraduates has been dominated by medical statisticians, with little input from the doctors who will actually be using this knowledge and these skills after graduation. Furthermore, doctor's statistical training needs may have changed due to advances in information technology and the increasing importance of evidence-based medicine. Thus there exists a need to investigate the views of practising medical doctors as to the statistical training required for undergraduate medical students, based on their own use of these skills in daily practice. Methods A questionnaire was designed to investigate doctors' views about undergraduate training in statistics and the need for these skills in daily practice, with a view to informing future teaching. The questionnaire was emailed to all clinicians with a link to the University of East Anglia Medical School. Open ended questions were included to elicit doctors' opinions about both their own undergraduate training in statistics and recommendations for the training of current medical students. Content analysis was performed by two of the authors to systematically categorise and describe all the responses provided by participants. Results 130 doctors responded, including both hospital consultants and general practitioners. The findings indicated that most had not recognised the value of their undergraduate teaching in statistics and probability at the time, but had subsequently found the skills relevant to their career. Suggestions for improving undergraduate teaching in these areas included referring to actual research and ensuring relevance to, and integration with, clinical practice. Conclusions Grounding the teaching of statistics
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
Woods and Russell, Hill, and the emergence of medical statistics
Farewell, Vern; Johnson, Tony
2010-01-01
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. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20535761
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.
Directory of Open Access Journals (Sweden)
Lauren D Arnold
Full Text Available BACKGROUND: Statistical training across the continuum of medical education may not have advanced at the pace of statistical reporting in the medical literature, yet a comprehensive understanding of statistical concepts most commonly presented in current research is critical to the effective practice of Evidence Based Medicine. The objective of this content analysis was to describe statistical techniques used in a leading medical journal, JAMA, across a 20-year period, with a focus on implications for medical education. METHODS AND FINDINGS: Two issues of JAMA published each month in 1990, 2000, and 2010 were randomly selected; from these, 361 articles were reviewed. Primary focus, study design, and statistical components were abstracted and examined by year of publication. The number of published RCTs and cohort studies differed significantly across years of interest, with an increasing trend of publication. The most commonly reported statistics over the 20-year period of interest included measures of morbidity and mortality, descriptive statistics, and epidemiologic outcomes. However, between 1990 and 2010, there was an increase in reporting of more advanced methods, such as multivariable regression, multilevel modeling, survival analysis, and sensitivity analysis. While this study is limited by a focus on one specific journal, a strength is that the journal examined is widely read by a range of clinical specialties and is considered a leading journal in the medical field, setting standards for published research. CONCLUSIONS: The increases in frequency and complexity of statistical reporting in the literature over the past two decades may suggest that moving beyond basic statistical concepts to a more comprehensive understanding of statistical methods is an important component of clinicians' ability to effectively read and use the medical research. These findings provide information to consider as medical schools and graduate medical education
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.
Statistical bootstrap model and annihilations
Möhring, H J
1974-01-01
The statistical bootstrap model (SBM) describes the decay of single, high mass, hadronic states (fireballs, clusters) into stable particles. Coupling constants B, one for each isospin multiplet of stable particles, are the only free parameter of the model. They are related to the maximum temperature parameter T/sub 0/. The various versions of the SMB can be classified into two groups: full statistical bootstrap models and linear ones. The main results of the model are the following: i) All momentum spectra are isotropic; especially the exclusive ones are described by invariant phase space. The inclusive and semi-inclusive single-particle distributions are asymptotically of pure exponential shape; the slope is governed by T /sub 0/ only. ii) The model parameter B for pions has been obtained by fitting the multiplicity distribution in pp and pn at rest, and corresponds to T/sub 0/=0.167 GeV in the full SBM with exotics. The average pi /sup -/ multiplicity for the linear and the full SBM (both with exotics) is c...
Statistical models for trisomic phenotypes
Energy Technology Data Exchange (ETDEWEB)
Lamb, N.E.; Sherman, S.L.; Feingold, E. [Emory Univ., Atlanta, GA (United States)
1996-01-01
Certain genetic disorders are rare in the general population but more common in individuals with specific trisomies, which suggests that the genes involved in the etiology of these disorders may be located on the trisomic chromosome. As with all aneuploid syndromes, however, a considerable degree of variation exists within each phenotype so that any given trait is present only among a subset of the trisomic population. We have previously presented a simple gene-dosage model to explain this phenotypic variation and developed a strategy to map genes for such traits. The mapping strategy does not depend on the simple model but works in theory under any model that predicts that affected individuals have an increased likelihood of disomic homozygosity at the trait locus. This paper explores the robustness of our mapping method by investigating what kinds of models give an expected increase in disomic homozygosity. We describe a number of basic statistical models for trisomic phenotypes. Some of these are logical extensions of standard models for disomic phenotypes, and some are more specific to trisomy. Where possible, we discuss genetic mechanisms applicable to each model. We investigate which models and which parameter values give an expected increase in disomic homozygosity in individuals with the trait. Finally, we determine the sample sizes required to identify the increased disomic homozygosity under each model. Most of the models we explore yield detectable increases in disomic homozygosity for some reasonable range of parameter values, usually corresponding to smaller trait frequencies. It therefore appears that our mapping method should be effective for a wide variety of moderately infrequent traits, even though the exact mode of inheritance is unlikely to be known. 21 refs., 8 figs., 1 tab.
Statistical shape and appearance models in osteoporosis.
Castro-Mateos, Isaac; Pozo, Jose M; Cootes, Timothy F; Wilkinson, J Mark; Eastell, Richard; Frangi, Alejandro F
2014-06-01
Statistical models (SMs) of shape (SSM) and appearance (SAM) have been acquiring popularity in medical image analysis since they were introduced in the early 1990s. They have been primarily used for segmentation, but they are also a powerful tool for 3D reconstruction and classification. All these tasks may be required in the osteoporosis domain, where fracture detection and risk estimation are key to reducing the mortality and/or morbidity of this bone disease. In this article, we review the different applications of SSMs and SAMs in the context of osteoporosis, and it concludes with a discussion of their advantages and disadvantages for this application.
Bayesian Model Selection and Statistical Modeling
Ando, Tomohiro
2010-01-01
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal lik
[Evaluation of using statistical methods in selected national medical journals].
Sych, Z
1996-01-01
The paper covers the performed evaluation of frequency with which the statistical methods were applied in analyzed works having been published in six selected, national medical journals in the years 1988-1992. For analysis the following journals were chosen, namely: Klinika Oczna, Medycyna Pracy, Pediatria Polska, Polski Tygodnik Lekarski, Roczniki Państwowego Zakładu Higieny, Zdrowie Publiczne. Appropriate number of works up to the average in the remaining medical journals was randomly selected from respective volumes of Pol. Tyg. Lek. The studies did not include works wherein the statistical analysis was not implemented, which referred both to national and international publications. That exemption was also extended to review papers, casuistic ones, reviews of books, handbooks, monographies, reports from scientific congresses, as well as papers on historical topics. The number of works was defined in each volume. Next, analysis was performed to establish the mode of finding out a suitable sample in respective studies, differentiating two categories: random and target selections. Attention was also paid to the presence of control sample in the individual works. In the analysis attention was also focussed on the existence of sample characteristics, setting up three categories: complete, partial and lacking. In evaluating the analyzed works an effort was made to present the results of studies in tables and figures (Tab. 1, 3). Analysis was accomplished with regard to the rate of employing statistical methods in analyzed works in relevant volumes of six selected, national medical journals for the years 1988-1992, simultaneously determining the number of works, in which no statistical methods were used. Concurrently the frequency of applying the individual statistical methods was analyzed in the scrutinized works. Prominence was given to fundamental statistical methods in the field of descriptive statistics (measures of position, measures of dispersion) as well as
Statistical Analysis by Statistical Physics Model for the STOCK Markets
Wang, Tiansong; Wang, Jun; Fan, Bingli
A new stochastic stock price model of stock markets based on the contact process of the statistical physics systems is presented in this paper, where the contact model is a continuous time Markov process, one interpretation of this model is as a model for the spread of an infection. Through this model, the statistical properties of Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) are studied. In the present paper, the data of SSE Composite Index and the data of SZSE Component Index are analyzed, and the corresponding simulation is made by the computer computation. Further, we investigate the statistical properties, fat-tail phenomena, the power-law distributions, and the long memory of returns for these indices. The techniques of skewness-kurtosis test, Kolmogorov-Smirnov test, and R/S analysis are applied to study the fluctuation characters of the stock price returns.
Visualizing statistical models and concepts
Farebrother, RW
2002-01-01
Examines classic algorithms, geometric diagrams, and mechanical principles for enhancing visualization of statistical estimation procedures and mathematical concepts in physics, engineering, and computer programming.
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.
Teaching statistics to medical undergraduates using interactive and participatory sessions
Directory of Open Access Journals (Sweden)
THULASINGAM MAHALAKSHMY
2013-10-01
Full Text Available Introduction: In India, medical undergraduates think that statistics is difficult to understand. Often, it is taught just before final assessment examination using didactic lectures, with little use of medical examples and less focus on application. Hence, we prepared interactive, participatory sessions for teaching biostatistics to medical undergraduate. Methods: The sessions were delivered by a facilitator. It had clearly specified objectives and teaching learning strategies. A needs assessment was done by interviewing the students who had undergone traditional biostatistics teaching methodology. Specific learning objectives for the sessions were finalized using the Delphi technique and review of University syllabus. Two trained Community Medicine faculties designed the lesson plans ‘backwards’ from desired outcome to content, teaching/learning strategies, assessment and evaluation process (Outcomes-based lesson planning. Forty, third-semester (Para-clinical phase of the second year medical undergraduates undertook these seven teaching sessions. The session followed adult learning principles and included group discussions, games and reflections. We evaluated the impact of the sessions using in-depth interviews, retrospective post-then-preself- assessment and a pre-announced written test. Results: With traditional statistics teaching methodology, students perceived it as a standalone subject and were not interested in statistics. Students who underwent the sessions commented that the sessions were enjoyable, interesting, and participatory and more than %90 of them felt they were engaged throughout the session. They also narrated various instances where they could apply the biostatistics learning. In the post-then-pre-assessment median post-session scores for all the objectives were significantly higher (p <0.050. Conclusion: Use of interactive, participatory sessions for teaching biostatistics to medical undergraduates resulted in a
Fermi breakup and the statistical multifragmentation model
Energy Technology Data Exchange (ETDEWEB)
Carlson, B.V., E-mail: brett@ita.br [Departamento de Fisica, Instituto Tecnologico de Aeronautica - CTA, 12228-900 Sao Jose dos Campos (Brazil); Donangelo, R. [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Cidade Universitaria, CP 68528, 21941-972, Rio de Janeiro (Brazil); Instituto de Fisica, Facultad de Ingenieria, Universidad de la Republica, Julio Herrera y Reissig 565, 11.300 Montevideo (Uruguay); Souza, S.R. [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Cidade Universitaria, CP 68528, 21941-972, Rio de Janeiro (Brazil); Instituto de Fisica, Universidade Federal do Rio Grande do Sul, Av. Bento Goncalves 9500, CP 15051, 91501-970, Porto Alegre (Brazil); Lynch, W.G.; Steiner, A.W.; Tsang, M.B. [Joint Institute for Nuclear Astrophysics, National Superconducting Cyclotron Laboratory and the Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States)
2012-02-15
We demonstrate the equivalence of a generalized Fermi breakup model, in which densities of excited states are taken into account, to the microcanonical statistical multifragmentation model used to describe the disintegration of highly excited fragments of nuclear reactions. We argue that such a model better fulfills the hypothesis of statistical equilibrium than the Fermi breakup model generally used to describe statistical disintegration of light mass nuclei.
Directory of Open Access Journals (Sweden)
Harris Alex HS
2011-08-01
Full Text Available Abstract Background To assist educators and researchers in improving the quality of medical research, we surveyed the editors and statistical reviewers of high-impact medical journals to ascertain the most frequent and critical statistical errors in submitted manuscripts. Findings The Editors-in-Chief and statistical reviewers of the 38 medical journals with the highest impact factor in the 2007 Science Journal Citation Report and the 2007 Social Science Journal Citation Report were invited to complete an online survey about the statistical and design problems they most frequently found in manuscripts. Content analysis of the responses identified major issues. Editors and statistical reviewers (n = 25 from 20 journals responded. Respondents described problems that we classified into two, broad themes: A. statistical and sampling issues and B. inadequate reporting clarity or completeness. Problems included in the first theme were (1 inappropriate or incomplete analysis, including violations of model assumptions and analysis errors, (2 uninformed use of propensity scores, (3 failing to account for clustering in data analysis, (4 improperly addressing missing data, and (5 power/sample size concerns. Issues subsumed under the second theme were (1 Inadequate description of the methods and analysis and (2 Misstatement of results, including undue emphasis on p-values and incorrect inferences and interpretations. Conclusions The scientific quality of submitted manuscripts would increase if researchers addressed these common design, analytical, and reporting issues. Improving the application and presentation of quantitative methods in scholarly manuscripts is essential to advancing medical research.
Statistical modelling of fish stocks
DEFF Research Database (Denmark)
Kvist, Trine
1999-01-01
for modelling the dynamics of a fish population is suggested. A new approach is introduced to analyse the sources of variation in age composition data, which is one of the most important sources of information in the cohort based models for estimation of stock abundancies and mortalities. The approach combines...... and it is argued that an approach utilising stochastic differential equations might be advantagous in fish stoch assessments....
Statistical modelling of fish stocks
DEFF Research Database (Denmark)
Kvist, Trine
1999-01-01
for modelling the dynamics of a fish population is suggested. A new approach is introduced to analyse the sources of variation in age composition data, which is one of the most important sources of information in the cohort based models for estimation of stock abundancies and mortalities. The approach combines...... and it is argued that an approach utilising stochastic differential equations might be advantagous in fish stoch assessments....
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...
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,
Statistical Modeling of Bivariate Data.
1982-08-01
end identify by lock nsum br) joint density-quantile function, dependence-density, non-parametric bivariate density estimation, entropy , exponential...estimated, by autoregressive or exponential model estimators I with maximum entropy properties, is investigated in this thesis. The results provide...important and useful procedures for nonparametric bivariate density estimation. The thesis discusses estimators of the entropy H(d) of ul2) which seem to me
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...
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.
Accelerated life models modeling and statistical analysis
Bagdonavicius, Vilijandas
2001-01-01
Failure Time DistributionsIntroductionParametric Classes of Failure Time DistributionsAccelerated Life ModelsIntroductionGeneralized Sedyakin's ModelAccelerated Failure Time ModelProportional Hazards ModelGeneralized Proportional Hazards ModelsGeneralized Additive and Additive-Multiplicative Hazards ModelsChanging Shape and Scale ModelsGeneralizationsModels Including Switch-Up and Cycling EffectsHeredity HypothesisSummaryAccelerated Degradation ModelsIntroductionDegradation ModelsModeling the Influence of Explanatory Varia
Multistructure Statistical Model Applied To Factor Analysis
Bentler, Peter M.
1976-01-01
A general statistical model for the multivariate analysis of mean and covariance structures is described. Matrix calculus is used to develop the statistical aspects of one new special case in detail. This special case separates the confounding of principal components and factor analysis. (DEP)
Picat, M-Q; Savès, M; Asselineau, J; Dumoulin, M; Coureau, G; Salmi, L-R; Perez, P; Chêne, G
2013-06-01
The main source of key medical information consists in original articles published in peer-reviewed biomedical journals. Reported studies use increasingly sophisticated statistical and epidemiological approaches that first require a solid understanding of core methods. However, such understanding is not widely shared among physicians. Our aim was to assess whether the basic statistical and epidemiological methods used in original articles published in general biomedical journals are taught during the first years of the medical curriculum in France. We selected original articles published in The New England Journal of Medicine, The Lancet, and The Journal of the American Medical Association, over a period of six months in 2007 and in 2008. A standardized statistical content checklist was used to extract the necessary information in the "Abstract", "Methods", "Results", footnotes of tables, and legends of figures. The methods used in the selected articles were compared to the national program and the public health program of biostatistics and epidemiology taught during the first six years of medical school. The 237 analyzed original articles all used at least one statistical or epidemiological method. Descriptive statistics, confidence interval and Chi(2) or Fisher tests, methods used in more than 50% of articles, were repeatedly taught throughout the medicine curriculum. Measures of association, sample size, fit and Kaplan-Meier method, used in 40 to 50% of articles, were specifically taught during training sessions on critical reading methods. Cox model (41% of articles) and logistic regression (24% of articles) were never taught. The most widely used illustrations, contingency tables (92%) and flowcharts (48%), were not included in the national program. More teaching of the core methods underlying the understanding of sophisticated methods and illustrations should be included in the early medical curriculum so that physicians can read the scientific literature
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
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.
Semantic Importance Sampling for Statistical Model Checking
2015-01-16
approach called Statistical Model Checking (SMC) [16], which relies on Monte - Carlo -based simulations to solve this verification task more scalably...Conclusion Statistical model checking (SMC) is a prominent approach for rigorous analysis of stochastic systems using Monte - Carlo simulations. In this... Monte - Carlo simulations, for computing the bounded probability that a specific event occurs during a stochastic system’s execution. Estimating the
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...
Probability and Statistics in Sensor Performance Modeling
2010-12-01
transformed Rice- Nakagami distribution ......................................................................... 49 Report Documentation Page...acoustic or electromagnetic waves are scattered by both objects and turbulent wind. A version of the Rice- Nakagami model (specifically with a...Gaussian, lognormal, exponential, gamma, and the 2XX → transformed Rice- Nakagami —as well as a discrete model. (Other examples of statistical models
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...
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
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.
[Updated medical death documents: Medical Death Certificate and the Birth Statistics Bulletin].
Cirera, Lluís; Segura, Andreu
2010-08-01
Public health physicians have constantly urged that the Medical Death Certificate (CMD in Spain) and the notification of perinatal deaths be adapted to WHO standards. The new CMD came into effect in January 2009, whilst significant changes were made to the Birth Statistics Bulletin (BEP acronym in Spanish) in 2007. In this article the certifying doctor is informed on their novel and key aspects. The health significance of this medico-legal act is emphasised. At the same time associated issues are mentioned that still need to be resolved in Spain. The CMD unifies the medical certificate and the death statistics bulletin on one form and complies with WHO recommendations. It asks whether the death has been the result of an already registered traffic or work accident, and if an autopsy (clinical) has been performed. For place of death, it means the place where the cadaver has been recognised and the death certified. The approximate time of the causes of death must also be indicated. The BEP registers the births and deaths in labour. It includes the education level and occupation levels of the mother and father, and still has the number of weeks gestation and the birth weight in grams. Notification of legal deaths and perinatal deaths still need to be improved; the confidential transfer of causes of death to doctors and researchers; and death statistics according to cause with less delay between the events and their availability and publication.
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
Statistical Modeling for Radiation Hardness Assurance
Ladbury, Raymond L.
2014-01-01
We cover the models and statistics associated with single event effects (and total ionizing dose), why we need them, and how to use them: What models are used, what errors exist in real test data, and what the model allows us to say about the DUT will be discussed. In addition, how to use other sources of data such as historical, heritage, and similar part and how to apply experience, physics, and expert opinion to the analysis will be covered. Also included will be concepts of Bayesian statistics, data fitting, and bounding rates.
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......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....... 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...
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.
Statistical Model Checking for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand
2012-01-01
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique ap...
Dielectronic recombination rate in statistical model
Demura A.V.; Leontyev D.S.; Lisitsa V.S.; Shurigyn V.A.
2017-01-01
The dielectronic recombination rate of multielectron ions was calculated by means of the statistical approach. It is based on an idea of collective excitations of atomic electrons with the local plasma frequencies. These frequencies are expressed via the Thomas-Fermi model electron density distribution. The statistical approach provides fast computation of DR rates that are compared with the modern quantum mechanical calculations. The results are important for current studies of thermonuclear...
Dielectronic recombination rate in statistical model
Directory of Open Access Journals (Sweden)
Demura A.V.
2017-01-01
Full Text Available The dielectronic recombination rate of multielectron ions was calculated by means of the statistical approach. It is based on an idea of collective excitations of atomic electrons with the local plasma frequencies. These frequencies are expressed via the Thomas-Fermi model electron density distribution. The statistical approach provides fast computation of DR rates that are compared with the modern quantum mechanical calculations. The results are important for current studies of thermonuclear plasmas with the tungsten impurities.
Dielectronic recombination rate in statistical model
Demura, A. V.; Leontyev, D. S.; Lisitsa, V. S.; Shurigyn, V. A.
2016-12-01
The dielectronic recombination rate of multielectron ions was calculated by means of the statistical approach. It is based on an idea of collective excitations of atomic electrons with the local plasma frequencies. These frequencies are expressed via the Thomas-Fermi model electron density distribution. The statistical approach provides fast computation of DR rates that are compared with the modern quantum mechanical calculations. The results are important for current studies of thermonuclear plasmas with the tungsten impurities.
Mesoscopic full counting statistics and exclusion models
Roche, P.-E.; Derrida, B.; Douçot, B.
2005-02-01
We calculate the distribution of current fluctuations in two simple exclusion models. Although these models are classical, we recover even for small systems such as a simple or a double barrier, the same distibution of current as given by traditional formalisms for quantum mesoscopic conductors. Due to their simplicity, the full counting statistics in exclusion models can be reduced to the calculation of the largest eigenvalue of a matrix, the size of which is the number of internal configurations of the system. As examples, we derive the shot noise power and higher order statistics of current fluctuations (skewness, full counting statistics, ....) of various conductors, including multiple barriers, diffusive islands between tunnel barriers and diffusive media. A special attention is dedicated to the third cumulant, which experimental measurability has been demonstrated lately.
Three Generative, Lexicalised Models for Statistical Parsing
Collins, M
1997-01-01
In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both subcategorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (Collins 96).
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)
2017-03-23
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.
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
An R companion to linear statistical models
Hay-Jahans, Christopher
2011-01-01
Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cove
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
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.
STATISTICAL MODELS OF REPRESENTING INTELLECTUAL CAPITAL
Directory of Open Access Journals (Sweden)
Andreea Feraru
2016-07-01
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.
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.
Statistical Modeling Efforts for Headspace Gas
Energy Technology Data Exchange (ETDEWEB)
Weaver, Brian Phillip [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-03-17
The purpose of this document is to describe the statistical modeling effort for gas concentrations in WIPP storage containers. The concentration (in ppm) of CO_{2} in the headspace volume of standard waste box (SWB) 68685 is shown. A Bayesian approach and an adaptive Metropolis-Hastings algorithm were used.
Nonperturbative approach to the modified statistical model
Energy Technology Data Exchange (ETDEWEB)
Magdy, M.A.; Bekmezci, A.; Sever, R. [Middle East Technical Univ., Ankara (Turkey)
1993-12-01
The modified form of the statistical model is used without making any perturbation. The mass spectra of the lowest S, P and D levels of the (Q{bar Q}) and the non-self-conjugate (Q{bar q}) mesons are studied with the Song-Lin potential. The authors results are in good agreement with the experimental and theoretical findings.
Statistical Model Checking for Stochastic Hybrid Systems
DEFF Research Database (Denmark)
David, Alexandre; Du, Dehui; Larsen, Kim Guldstrand
2012-01-01
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique...... applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings....
Statistical modeling of space shuttle environmental data
Tubbs, J. D.; Brewer, D. W.
1983-01-01
Statistical models which use a class of bivariate gamma distribution are examined. Topics discussed include: (1) the ratio of positively correlated gamma varieties; (2) a method to determine if unequal shape parameters are necessary in bivariate gamma distribution; (3) differential equations for modal location of a family of bivariate gamma distribution; and (4) analysis of some wind gust data using the analytical results developed for modeling application.
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
Statistical physical models of cellular motility
Banigan, Edward J.
Cellular motility is required for a wide range of biological behaviors and functions, and the topic poses a number of interesting physical questions. In this work, we construct and analyze models of various aspects of cellular motility using tools and ideas from statistical physics. We begin with a Brownian dynamics model for actin-polymerization-driven motility, which is responsible for cell crawling and "rocketing" motility of pathogens. Within this model, we explore the robustness of self-diffusiophoresis, which is a general mechanism of motility. Using this mechanism, an object such as a cell catalyzes a reaction that generates a steady-state concentration gradient that propels the object in a particular direction. We then apply these ideas to a model for depolymerization-driven motility during bacterial chromosome segregation. We find that depolymerization and protein-protein binding interactions alone are sufficient to robustly pull a chromosome, even against large loads. Next, we investigate how forces and kinetics interact during eukaryotic mitosis with a many-microtubule model. Microtubules exert forces on chromosomes, but since individual microtubules grow and shrink in a force-dependent way, these forces lead to bistable collective microtubule dynamics, which provides a mechanism for chromosome oscillations and microtubule-based tension sensing. Finally, we explore kinematic aspects of cell motility in the context of the immune system. We develop quantitative methods for analyzing cell migration statistics collected during imaging experiments. We find that during chronic infection in the brain, T cells run and pause stochastically, following the statistics of a generalized Levy walk. These statistics may contribute to immune function by mimicking an evolutionarily conserved efficient search strategy. Additionally, we find that naive T cells migrating in lymph nodes also obey non-Gaussian statistics. Altogether, our work demonstrates how physical
Distributed Object Medical Imaging Model
Noor, Ahmad Shukri Mohd
2009-01-01
Digital medical informatics and images are commonly used in hospitals today,. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM) to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients' data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common...
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.
Pitfalls in statistical landslide susceptibility modelling
Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut
2010-05-01
The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible
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...
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.
Statistical Compressed Sensing of Gaussian Mixture Models
Yu, Guoshen
2011-01-01
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is introduced. SCS based on Gaussian models is investigated in depth. For signals that follow a single Gaussian model, with Gaussian or Bernoulli sensing matrices of O(k) measurements, considerably smaller than the O(k log(N/k)) required by conventional CS based on sparse models, where N is the signal dimension, and with an optimal decoder implemented via linear filtering, significantly faster than the pursuit decoders applied in conventional CS, the error of SCS is shown tightly upper bounded by a constant times the best k-term approximation error, with overwhelming probability. The failure probability is also significantly smaller than that of conventional sparsity-oriented CS. Stronger yet simpler results further show that for any sensing matrix, the error of Gaussian SCS is u...
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....
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.
Statistical Language Model for Chinese Text Proofreading
Institute of Scientific and Technical Information of China (English)
张仰森; 曹元大
2003-01-01
Statistical language modeling techniques are investigated so as to construct a language model for Chinese text proofreading. After the defects of n-gram model are analyzed, a novel statistical language model for Chinese text proofreading is proposed. This model takes full account of the information located before and after the target word wi, and the relationship between un-neighboring words wi and wj in linguistic environment(LE). First, the word association degree between wi and wj is defined by using the distance-weighted factor, wj is l words apart from wi in the LE, then Bayes formula is used to calculate the LE related degree of word wi, and lastly, the LE related degree is taken as criterion to predict the reasonability of word wi that appears in context. Comparing the proposed model with the traditional n-gram in a Chinese text automatic error detection system, the experiments results show that the error detection recall rate and precision rate of the system have been improved.
Statistics, Computation, and Modeling in Cosmology
Jewell, Jeff; Guiness, Joe; SAMSI 2016 Working Group in Cosmology
2017-01-01
Current and future ground and space based missions are designed to not only detect, but map out with increasing precision, details of the universe in its infancy to the present-day. As a result we are faced with the challenge of analyzing and interpreting observations from a wide variety of instruments to form a coherent view of the universe. Finding solutions to a broad range of challenging inference problems in cosmology is one of the goals of the “Statistics, Computation, and Modeling in Cosmology” workings groups, formed as part of the year long program on ‘Statistical, Mathematical, and Computational Methods for Astronomy’, hosted by the Statistical and Applied Mathematical Sciences Institute (SAMSI), a National Science Foundation funded institute. Two application areas have emerged for focused development in the cosmology working group involving advanced algorithmic implementations of exact Bayesian inference for the Cosmic Microwave Background, and statistical modeling of galaxy formation. The former includes study and development of advanced Markov Chain Monte Carlo algorithms designed to confront challenging inference problems including inference for spatial Gaussian random fields in the presence of sources of galactic emission (an example of a source separation problem). Extending these methods to future redshift survey data probing the nonlinear regime of large scale structure formation is also included in the working group activities. In addition, the working group is also focused on the study of ‘Galacticus’, a galaxy formation model applied to dark matter-only cosmological N-body simulations operating on time-dependent halo merger trees. The working group is interested in calibrating the Galacticus model to match statistics of galaxy survey observations; specifically stellar mass functions, luminosity functions, and color-color diagrams. The group will use subsampling approaches and fractional factorial designs to statistically and
Statistical assessment of predictive modeling uncertainty
Barzaghi, Riccardo; Marotta, Anna Maria
2017-04-01
When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. We propose a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical finite element model in the Mediterranean region. Using a novel χ2 analysis in which both data and model uncertainties are taken into account, the model's estimated tectonic strain pattern due to the Africa-Eurasia convergence in the area that extends from the Calabrian Arc to the Alpine domain is compared with that estimated from GPS velocities while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values that have better statistical significance and might help a sharper identification of the best-fitting geophysical models.
Statistical Seasonal Sea Surface based Prediction Model
Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima
2014-05-01
The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.
Statistical modelling for falls count data.
Ullah, Shahid; Finch, Caroline F; Day, Lesley
2010-03-01
Falls and their injury outcomes have count distributions that are highly skewed toward the right with clumping at zero, posing analytical challenges. Different modelling approaches have been used in the published literature to describe falls count distributions, often without consideration of the underlying statistical and modelling assumptions. This paper compares the use of modified Poisson and negative binomial (NB) models as alternatives to Poisson (P) regression, for the analysis of fall outcome counts. Four different count-based regression models (P, NB, zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB)) were each individually fitted to four separate fall count datasets from Australia, New Zealand and United States. The finite mixtures of P and NB regression models were also compared to the standard NB model. Both analytical (F, Vuong and bootstrap tests) and graphical approaches were used to select and compare models. Simulation studies assessed the size and power of each model fit. This study confirms that falls count distributions are over-dispersed, but not dispersed due to excess zero counts or heterogeneous population. Accordingly, the P model generally provided the poorest fit to all datasets. The fit improved significantly with NB and both zero-inflated models. The fit was also improved with the NB model, compared to finite mixtures of both P and NB regression models. Although there was little difference in fit between NB and ZINB models, in the interests of parsimony it is recommended that future studies involving modelling of falls count data routinely use the NB models in preference to the P or ZINB or finite mixture distribution. The fact that these conclusions apply across four separate datasets from four different samples of older people participating in studies of different methodology, adds strength to this general guiding principle.
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 relation to the so-called uniform infinite tree and results on the Hausdorff and spectral dimension of two-dimensional space-time obtained in B. Durhuus, T. Jonsson, J.F. Wheater, J. Stat. Phys. 139, 859 (2010) are briefly outlined. For the latter we discuss results on the absence of spontaneous...... magnetization and argue that, in the generic case, the values of the Hausdorff and spectral dimension of the underlying infinite trees are not influenced by the coupling to an Ising model in a constant magnetic field (B. Durhuus, G.M. Napolitano, in preparation)...
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...
A survey of statistical network models
Goldenberg, Anna; Fienberg, Stephen E; Airoldi, Edoardo M
2009-01-01
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry poin...
Distributed Object Medical Imaging Model
Directory of Open Access Journals (Sweden)
Ahmad Shukri Mohd Noor
2009-09-01
Full Text Available Digital medical informatics and images are commonly used in hospitals today. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients' data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common Object Request Broker Architecture (CORBA, Java Database Connectivity (JDBC, and Java language provide the capability to combine the DOMIM resources into an integrated, interoperable, and scalable system. The underneath technology, including IDL ORB, Event Service, IIOP JDBC/ODBC, legacy system wrapping and Java implementation are explored. This paper explores a distributed collaborative CORBA/JDBC based framework that will enhance medical information management requirements and development. It encompasses a new paradigm for the delivery of health services that requires process reengineering, cultural changes, as well as organizational changes.
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
Electronic noise modeling in statistical iterative reconstruction.
Xu, Jingyan; Tsui, Benjamin M W
2009-06-01
We consider electronic noise modeling in tomographic image reconstruction when the measured signal is the sum of a Gaussian distributed electronic noise component and another random variable whose log-likelihood function satisfies a certain linearity condition. Examples of such likelihood functions include the Poisson distribution and an exponential dispersion (ED) model that can approximate the signal statistics in integration mode X-ray detectors. We formulate the image reconstruction problem as a maximum-likelihood estimation problem. Using an expectation-maximization approach, we demonstrate that a reconstruction algorithm can be obtained following a simple substitution rule from the one previously derived without electronic noise considerations. To illustrate the applicability of the substitution rule, we present examples of a fully iterative reconstruction algorithm and a sinogram smoothing algorithm both in transmission CT reconstruction when the measured signal contains additive electronic noise. Our simulation studies show the potential usefulness of accurate electronic noise modeling in low-dose CT applications.
Statistical model with a standard Γ distribution
Patriarca, Marco; Chakraborti, Anirban; Kaski, Kimmo
2004-07-01
We study a statistical model consisting of N basic units which interact with each other by exchanging a physical entity, according to a given microscopic random law, depending on a parameter λ . We focus on the equilibrium or stationary distribution of the entity exchanged and verify through numerical fitting of the simulation data that the final form of the equilibrium distribution is that of a standard Gamma distribution. The model can be interpreted as a simple closed economy in which economic agents trade money and a saving criterion is fixed by the saving propensity λ . Alternatively, from the nature of the equilibrium distribution, we show that the model can also be interpreted as a perfect gas at an effective temperature T(λ) , where particles exchange energy in a space with an effective dimension D(λ) .
Statistical model with a standard Gamma distribution
Chakraborti, Anirban; Patriarca, Marco
2005-03-01
We study a statistical model consisting of N basic units which interact with each other by exchanging a physical entity, according to a given microscopic random law, depending on a parameter λ. We focus on the equilibrium or stationary distribution of the entity exchanged and verify through numerical fitting of the simulation data that the final form of the equilibrium distribution is that of a standard Gamma distribution. The model can be interpreted as a simple closed economy in which economic agents trade money and a saving criterion is fixed by the saving propensity λ. Alternatively, from the nature of the equilibrium distribution, we show that the model can also be interpreted as a perfect gas at an effective temperature T (λ), where particles exchange energy in a space with an effective dimension D (λ).
Statistical Decision-Tree Models for Parsing
Magerman, D M
1995-01-01
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to text-processing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too complex and detailed to develop manually for most interesting domains; (2) parsing models must rely heavily on lexical and contextual information to analyze sentences accurately; and (3) existing {$n$}-gram modeling techniques are inadequate for parsing models. In experiments comparing SPATTER with IBM's computer manuals parser, SPATTER significantly outperforms the grammar-based parser. Evaluating SPATTER against the Penn Treebank Wall ...
Statistical Model Checking for Product Lines
DEFF Research Database (Denmark)
ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto
2016-01-01
average cost of products (in terms of the attributes of the products’ features) and the probability of features to be (un)installed at runtime. The product lines must be modelled in QFLan, which extends the probabilistic feature-oriented language PFLan with novel quantitative constraints among features......We report on the suitability of statistical model checking for the analysis of quantitative properties of product line models by an extended treatment of earlier work by the authors. The type of analysis that can be performed includes the likelihood of specific product behaviour, the expected...... and on behaviour and with advanced feature installation options. QFLan is a rich process-algebraic specification language whose operational behaviour interacts with a store of constraints, neatly separating product configuration from product behaviour. The resulting probabilistic configurations and probabilistic...
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.
Challenges in Dental Statistics: Data and Modelling
Directory of Open Access Journals (Sweden)
Domenica Matranga
2013-03-01
Full Text Available The aim of this work is to present the reflections and proposals derived from the first Workshop of the SISMEC STATDENT working group on statistical methods and applications in dentistry, held in Ancona (Italy on 28th September 2011. STATDENT began as a forum of comparison and discussion for statisticians working in the field of dental research in order to suggest new and improve existing biostatistical and clinical epidemiological methods. During the meeting, we dealt with very important topics of statistical methodology for the analysis of dental data, covering the analysis of hierarchically structured and over-dispersed data, the issue of calibration and reproducibility, as well as some problems related to survey methodology, such as the design and construction of unbiased statistical indicators and of well conducted clinical trials. This paper gathers some of the methodological topics discussed during the meeting, concerning multilevel and zero-inflated models for the analysis of caries data and methods for the training and calibration of raters in dental epidemiology.
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...... proved very useful for identifying interesting properties of biological systems. Our aim is to offer the best of the two worlds: optimal domain specific interfaces and formalisms suited to biology combined with powerful SMC analysis techniques for stochastic and hybrid systems. This goal is obtained...
A Statistical Model of Skewed Associativity
Michaud, Pierre
2002-01-01
This paper presents a statistical model of set-associativity, victim caching and skewed-associativity, with an emphasis on skewed-associativity. We show that set-associativity is not efficient when the working-set size is close to the cache size. We refer to this as the unit working-set problem. We show that victim-caching is not a practical solution to the unit working-se- t problem either, although victim caching emulates full associativity for working-sets much larger than the victim buffe...
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.
Statistical tests of simple earthquake cycle models
DeVries, Phoebe M. R.; Evans, Eileen L.
2016-12-01
A central goal of observing and modeling the earthquake cycle is to forecast when a particular fault may generate an earthquake: a fault late in its earthquake cycle may be more likely to generate an earthquake than a fault early in its earthquake cycle. Models that can explain geodetic observations throughout the entire earthquake cycle may be required to gain a more complete understanding of relevant physics and phenomenology. Previous efforts to develop unified earthquake models for strike-slip faults have largely focused on explaining both preseismic and postseismic geodetic observations available across a few faults in California, Turkey, and Tibet. An alternative approach leverages the global distribution of geodetic and geologic slip rate estimates on strike-slip faults worldwide. Here we use the Kolmogorov-Smirnov test for similarity of distributions to infer, in a statistically rigorous manner, viscoelastic earthquake cycle models that are inconsistent with 15 sets of observations across major strike-slip faults. We reject a large subset of two-layer models incorporating Burgers rheologies at a significance level of α = 0.05 (those with long-term Maxwell viscosities ηM 4.6 × 1020 Pa s) but cannot reject models on the basis of transient Kelvin viscosity ηK. Finally, we examine the implications of these results for the predicted earthquake cycle timing of the 15 faults considered and compare these predictions to the geologic and historical record.
Projecting Policy Effects with Statistical Models Projecting Policy Effects with Statistical Models
Directory of Open Access Journals (Sweden)
Christopher Sims
1988-03-01
Full Text Available This paper attempts to briefly discus the current frontiers in quantitative modeling for forecastina and policy analvsis. It does so by summarizing some recent developmenrs in three areas: reduced form forecasting models; theoretical models including elements of stochastic optimization; and identification. In the process, the paper tries to provide some remarks on the direction we seem to be headed. Projecting Policy Effects with Statistical Models
Statistical Mechanical Models of Integer Factorization Problem
Nakajima, Chihiro H.; Ohzeki, Masayuki
2017-01-01
We formulate the integer factorization problem via a formulation of the searching problem for the ground state of a statistical mechanical Hamiltonian. The first passage time required to find a correct divisor of a composite number signifies the exponential computational hardness. The analysis of the density of states of two macroscopic quantities, i.e., the energy and the Hamming distance from the correct solutions, leads to the conclusion that the ground state (correct solution) is completely isolated from the other low-energy states, with the distance being proportional to the system size. In addition, the profile of the microcanonical entropy of the model has two peculiar features that are each related to two marked changes in the energy region sampled via Monte Carlo simulation or simulated annealing. Hence, we find a peculiar first-order phase transition in our model.
Statistical model semiquantitatively approximates arabinoxylooligosaccharides' structural diversity
DEFF Research Database (Denmark)
Dotsenko, Gleb; Nielsen, Michael Krogsgaard; Lange, Lene
2016-01-01
A statistical model describing the random distribution of substituted xylopyranosyl residues in arabinoxylooligosaccharides is suggested and compared with existing experimental data. Structural diversity of arabinoxylooligosaccharides of various length, originating from different arabinoxylans...... (wheat flour arabinoxylan (arabinose/xylose, A/X = 0.47); grass arabinoxylan (A/X = 0.24); wheat straw arabinoxylan (A/X = 0.15); and hydrothermally pretreated wheat straw arabinoxylan (A/X = 0.05)), is semiquantitatively approximated using the proposed model. The suggested approach can be applied...... not only for prediction and quantification of arabinoxylooligosaccharides' structural diversity, but also for estimate of yield and selection of the optimal source of arabinoxylan for production of arabinoxylooligosaccharides with desired structural features....
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
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
Integrated statistical modelling of spatial landslide probability
Mergili, M.; Chu, H.-J.
2015-09-01
Statistical methods are commonly employed to estimate spatial probabilities of landslide release at the catchment or regional scale. Travel distances and impact areas are often computed by means of conceptual mass point models. The present work introduces a fully automated procedure extending and combining both concepts to compute an integrated spatial landslide probability: (i) the landslide inventory is subset into release and deposition zones. (ii) We employ a simple statistical approach to estimate the pixel-based landslide release probability. (iii) We use the cumulative probability density function of the angle of reach of the observed landslide pixels to assign an impact probability to each pixel. (iv) We introduce the zonal probability i.e. the spatial probability that at least one landslide pixel occurs within a zone of defined size. We quantify this relationship by a set of empirical curves. (v) The integrated spatial landslide probability is defined as the maximum of the release probability and the product of the impact probability and the zonal release probability relevant for each pixel. We demonstrate the approach with a 637 km2 study area in southern Taiwan, using an inventory of 1399 landslides triggered by the typhoon Morakot in 2009. We observe that (i) the average integrated spatial landslide probability over the entire study area corresponds reasonably well to the fraction of the observed landside area; (ii) the model performs moderately well in predicting the observed spatial landslide distribution; (iii) the size of the release zone (or any other zone of spatial aggregation) influences the integrated spatial landslide probability to a much higher degree than the pixel-based release probability; (iv) removing the largest landslides from the analysis leads to an enhanced model performance.
MSMBuilder: Statistical Models for Biomolecular Dynamics.
Harrigan, Matthew P; Sultan, Mohammad M; Hernández, Carlos X; Husic, Brooke E; Eastman, Peter; Schwantes, Christian R; Beauchamp, Kyle A; McGibbon, Robert T; Pande, Vijay S
2017-01-10
MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.
ZERODUR strength modeling with Weibull statistical distributions
Hartmann, Peter
2016-07-01
The decisive influence on breakage strength of brittle materials such as the low expansion glass ceramic ZERODUR is the surface condition. For polished or etched surfaces it is essential if micro cracks are present and how deep they are. Ground surfaces have many micro cracks caused by the generation process. Here only the depths of the micro cracks are relevant. In any case presence and depths of micro cracks are statistical by nature. The Weibull distribution is the model used traditionally for the representation of such data sets. It is based on the weakest link ansatz. The use of the two or three parameter Weibull distribution for data representation and reliability prediction depends on the underlying crack generation mechanisms. Before choosing the model for a specific evaluation, some checks should be done. Is there only one mechanism present or is it to be expected that an additional mechanism might contribute deviating results? For ground surfaces the main mechanism is the diamond grains' action on the surface. However, grains breaking from their bonding might be moved by the tool across the surface introducing a slightly deeper crack. It is not to be expected that these scratches follow the same statistical distribution as the grinding process. Hence, their description with the same distribution parameters is not adequate. Before including them a dedicated discussion should be performed. If there is additional information available influencing the selection of the model, for example the existence of a maximum crack depth, this should be taken into account also. Micro cracks introduced by small diamond grains on tools working with limited forces cannot be arbitrarily deep. For data obtained with such surfaces the existence of a threshold breakage stress should be part of the hypothesis. This leads to the use of the three parameter Weibull distribution. A differentiation based on the data set alone without preexisting information is possible but requires a
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.
Physical and Statistical Modeling of Saturn's Troposphere
Yanamandra-Fisher, Padmavati A.; Braverman, Amy J.; Orton, Glenn S.
2002-12-01
The 5.2-μm atmospheric window on Saturn is dominated by thermal radiation and weak gaseous absorption, with a 20% contribution from sunlight reflected from clouds. The striking variability displayed by Saturn's clouds at 5.2 μm and the detection of PH3 (an atmospheric tracer) variability near or below the 2-bar level and possibly at lower pressures provide salient constraints on the dynamical organization of Saturn's atmosphere by constraining the strength of vertical motions at two levels across the disk. We analyse the 5.2-μm spectra of Saturn by utilising two independent methods: (a) physical models based on the relevant atmospheric parameters and (b) statistical analysis, based on principal components analysis (PCA), to determine the influence of the variation of phosphine and the opacity of clouds deep within Saturn's atmosphere to understand the dynamics in its atmosphere.
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...... a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed...... as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation...
National Research Council Canada - National Science Library
Wu, Yazhou; Zhou, Liang; Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong
2015-01-01
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...
New advances in statistical modeling and applications
Santos, Rui; Oliveira, Maria; Paulino, Carlos
2014-01-01
This volume presents selected papers from the XIXth Congress of the Portuguese Statistical Society, held in the town of Nazaré, Portugal, from September 28 to October 1, 2011. All contributions were selected after a thorough peer-review process. It covers a broad range of papers in the areas of statistical science, probability and stochastic processes, extremes and statistical applications.
WE-A-201-02: Modern Statistical Modeling.
Niemierko, A
2016-06-01
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 Regulatory
Modelling earthquake interaction and seismicity statistics
Steacy, S.; Hetherington, A.
2009-04-01
The effects of earthquake interaction and fault complexity on seismicity statistics are investigated in a 3D model composed of a number of cellular automata (each representing an individual fault) distributed in a volume. Each automaton is assigned a fractal distribution of strength. Failure occurs when the 3D Coulomb stress on any cell exceeds its strength and stress transfer during simulated earthquake rupture is via nearest-neighbor rules formulated to give realistic stress concentrations. An event continues until all neighboring cells whose stresses exceed their strengths have ruptured and the size of the event is determined from its area and stress drop. Long-range stress interactions are computed following the termination of simulated ruptures using a boundary element code. In practice, these stress perturbations are only computed for events above a certain size (e.g. a threshold length of 10 km) and stresses are updated on nearby structures. Events which occur as a result of these stress interactions are considered to be "triggered" earthquakes and they, in turn, can trigger further seismic activity. The threshold length for computing interaction stresses is a free parameter and hence interaction can be "turned off" by setting this to an unrealistically high value. We consider 3 synthetic fault networks of increasing degrees of complexity - modelled on the North Anatolian fault system, the structures in the San Francisco Bay Area, and the Southern California fault network. We find that the effect of interaction is dramatically different in networks of differing complexity. In the North Anatolian analogue, for example, interaction leads to a decreased number of events, increased b-values, and an increase in recurrence intervals. In the Bay Area model, by contrast, we observe that interaction increases the number of events, decreases the b-values, and has little effect on recurrence intervals. For all networks, we find that interaction can activate mis
Pathway Model and Nonextensive Statistical Mechanics
Mathai, A. M.; Haubold, H. J.; Tsallis, C.
2015-12-01
The established technique of eliminating upper or lower parameters in a general hypergeometric series is profitably exploited to create pathways among confluent hypergeometric functions, binomial functions, Bessel functions, and exponential series. One such pathway, from the mathematical statistics point of view, results in distributions which naturally emerge within nonextensive statistical mechanics and Beck-Cohen superstatistics, as pursued in generalizations of Boltzmann-Gibbs statistics.
Statistical Ensemble Theory of Gompertz Growth Model
Directory of Open Access Journals (Sweden)
Takuya Yamano
2009-11-01
Full Text Available An ensemble formulation for the Gompertz growth function within the framework of statistical mechanics is presented, where the two growth parameters are assumed to be statistically distributed. The growth can be viewed as a self-referential process, which enables us to use the Bose-Einstein statistics picture. The analytical entropy expression pertain to the law can be obtained in terms of the growth velocity distribution as well as the Gompertz function itself for the whole process.
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.
Wu, Yazhou; Zhou, Liang; Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong
2015-01-01
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. This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. 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. 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. 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.
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
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.
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.
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......Software is in increasing fashion embedded within safety- and business critical processes of society. Errors in these embedded systems can lead to human casualties or severe monetary loss. Model checking technology has proven formal methods capable of finding and correcting errors in software....... However, software is approaching the boundary in terms of the complexity and size that model checking can handle. Furthermore, software systems are nowadays more frequently interacting with their environment hence accurately modelling such systems requires modelling the environment as well - resulting...
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)
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.
A probabilistic model for reducing medication errors.
Directory of Open Access Journals (Sweden)
Phung Anh Nguyen
Full Text Available BACKGROUND: Medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases. METHODS AND FINDINGS: Association rules of mining techniques are utilized for 103.5 million prescriptions from Taiwan's National Health Insurance database. The dataset included 204.5 million diagnoses with ICD9-CM codes and 347.7 million medications by using ATC codes. Disease-Medication (DM and Medication-Medication (MM associations were computed by their co-occurrence and associations' strength were measured by the interestingness or lift values which were being referred as Q values. The DMQs and MMQs were used to develop the AOP model to predict the appropriateness of a given prescription. Validation of this model was done by comparing the results of evaluation performed by the AOP model and verified by human experts. The results showed 96% accuracy for appropriate and 45% accuracy for inappropriate prescriptions, with a sensitivity and specificity of 75.9% and 89.5%, respectively. CONCLUSIONS: We successfully developed the AOP model as an efficient tool for automatic identification of uncommon or rare associations between disease-medication and medication-medication in prescriptions. The AOP model helps to reduce medication errors by alerting physicians, improving the patients' safety and the overall quality of care.
Meaney, Christopher; Moineddin, Rahim; Voruganti, Teja; O'Brien, Mary Ann; Krueger, Paul; Sullivan, Frank
2016-06-01
To describe trends in the use of statistical and epidemiological methods in the medical literature over the past 2 decades. We obtained all 1,028,786 articles from the PubMed Central Open-Access archive (retrieved May 9, 2015). We focused on 113,450 medical research articles. A Delphi panel identified 177 statistical/epidemiological methods pertinent to clinical researchers. We used a text-mining approach to determine if a specific statistical/epidemiological method was encountered in a given article. We report the proportion of articles using a specific method for the entire cross-sectional sample and also stratified into three blocks of time (1995-2005; 2006-2010; 2011-2015). Numeric descriptive statistics were commonplace (96.4% articles). Other frequently encountered methods groups included statistical inferential concepts (52.9% articles), epidemiological measures of association (53.5% articles) methods for diagnostic/classification accuracy (40.1% articles), hypothesis testing (28.8% articles), ANOVA (23.2% articles), and regression (22.6% articles). We observed relative percent increases in the use of: regression (103.0%), missing data methods (217.9%), survival analysis (147.6%), and correlated data analysis (192.2%). This study identified commonly encountered and emergent methods used to investigate medical research problems. Clinical researchers must be aware of the methodological landscape in their field, as statistical/epidemiological methods underpin research claims. Copyright © 2015 Elsevier Inc. All rights reserved.
Integer Set Compression and Statistical Modeling
DEFF Research Database (Denmark)
Larsson, N. Jesper
2014-01-01
Compression of integer sets and sequences has been extensively studied for settings where elements follow a uniform probability distribution. In addition, methods exist that exploit clustering of elements in order to achieve higher compression performance. In this work, we address the case where...... enumeration of elements may be arbitrary or random, but where statistics is kept in order to estimate probabilities of elements. We present a recursive subset-size encoding method that is able to benefit from statistics, explore the effects of permuting the enumeration order based on element probabilities...
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
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.
Statistical Compressive Sensing of Gaussian Mixture Models
Yu, Guoshen
2010-01-01
A new framework of compressive sensing (CS), namely statistical compressive sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution and achieving accurate reconstruction on average, is introduced. For signals following a Gaussian distribution, with Gaussian or Bernoulli sensing matrices of O(k) measurements, considerably smaller than the O(k log(N/k)) required by conventional CS, where N is the signal dimension, and with an optimal decoder implemented with linear filtering, significantly faster than the pursuit decoders applied in conventional CS, the error of SCS is shown tightly upper bounded by a constant times the k-best term approximation error, with overwhelming probability. The failure probability is also significantly smaller than that of conventional CS. Stronger yet simpler results further show that for any sensing matrix, the error of Gaussian SCS is upper bounded by a constant times the k-best term approximation with probability one, and the ...
Comparison of Statistical Models for Regional Crop Trial Analysis
Institute of Scientific and Technical Information of China (English)
ZHANG Qun-yuan; KONG Fan-ling
2002-01-01
Based on the review and comparison of main statistical analysis models for estimating varietyenvironment cell means in regional crop trials, a new statistical model, LR-PCA composite model was proposed, and the predictive precision of these models were compared by cross validation of an example data. Results showed that the order of model precision was LR-PCA model ＞ AMMI model ＞ PCA model ＞ Treatment Means (TM) model ＞ Linear Regression (LR) model ＞ Additive Main Effects ANOVA model. The precision gain factor of LR-PCA model was 1.55, increasing by 8.4% compared with AMMI.
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...
Research design and statistical methods in Indian medical journals: a retrospective survey.
Directory of Open Access Journals (Sweden)
Shabbeer Hassan
Full Text Available 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, p<0.0001 from 42.5% (250/588 to 56.7 % (439/774. The overall proportion of errors in study design decreased significantly (χ2=16.783, Φ=0.12 p<0.0001, 41.3% (243/588 compared to 30.6% (237/774. In 2013, randomized clinical trials designs 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, p<0.0001, 82.2% (263/320 compared to 66.3% (325/490 and
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 primar
Medical Models and Bayesian Networks
DEFF Research Database (Denmark)
Olesen, Kristian Grønborg
1999-01-01
Proc. of a Workshop Held during the Joint European Conf. on Artificial Intelligence in Medicine and Medical Decision Making : AIMDM'99, Aalborg, Denmark, June 1999......Proc. of a Workshop Held during the Joint European Conf. on Artificial Intelligence in Medicine and Medical Decision Making : AIMDM'99, Aalborg, Denmark, June 1999...
Medical Models and Bayesian Networks
DEFF Research Database (Denmark)
Olesen, Kristian Grønborg
1999-01-01
Proc. of a Workshop Held during the Joint European Conf. on Artificial Intelligence in Medicine and Medical Decision Making : AIMDM'99, Aalborg, Denmark, June 1999......Proc. of a Workshop Held during the Joint European Conf. on Artificial Intelligence in Medicine and Medical Decision Making : AIMDM'99, Aalborg, Denmark, June 1999...
Statistical models of shape optimisation and evaluation
Davies, Rhodri; Taylor, Chris
2014-01-01
Deformable shape models have wide application in computer vision and biomedical image analysis. This book addresses a key issue in shape modelling: establishment of a meaningful correspondence between a set of shapes. Full implementation details are provided.
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.
Statistics-based investigation on typhoon transition modeling
DEFF Research Database (Denmark)
Zhang, Shuoyun; Nishijima, Kazuyoshi
and the seasonality are taken into account by developing the models for different spatial grids and seasons separately. An appropriate size of spatial grids is investigated. The statistical characteristics of the random residual terms in the models are also examined. Finally, Monte Carlo simulations are performed......The present study revisits the statistical modeling of typhoon transition. The objective of the study is to provide insights on plausible statistical typhoon transition models based on extensive statistical analysis. First, the correlation structures of the typhoon transition are estimated in terms...
Statistical Tests for Mixed Linear Models
Khuri, André I; Sinha, Bimal K
2011-01-01
An advanced discussion of linear models with mixed or random effects. In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component models, generating much research activity that relies on linear models with mixed and random effects. This volume covers the most important research of the past decade as well as the latest developments in hypothesis testing. It compiles all currently available results in the area of exact and optimum tests for variance component models and offers the only comprehensive treatment for these models a
The reporting of statistics in medical educational studies: an observational study
Directory of Open Access Journals (Sweden)
Desbiens Norman A
2007-07-01
Full Text Available Abstract Background There is confusion in the medical literature as to whether statistics should be reported in survey studies that query an entire population, as is often done in educational studies. Our objective was to determine how often statistical tests have been reported in such articles in two prominent journals that publish these types of studies. Methods For this observational study, we used electronic searching to identify all survey studies published in Academic Medicine and the Journal of General Internal Medicine in which an entire population was studied. We tallied whether inferential statistics were used and whether p-values were reported. Results Eighty-four articles were found: 62 in Academic Medicine and 22 in the Journal of General Internal Medicine. Overall, 38 (45% of the articles reported or stated that they calculated statistics: 35% in Academic Medicine and 73% in the Journal of General Internal Medicine. Conclusion Educational enumeration surveys frequently report statistical tests. Until a better case can be made for doing so, a simple rule can be proffered to researchers. When studying an entire population (e.g., all program directors, all deans, and all medical schools for factual information, do not perform statistical tests. Reporting percentages is sufficient and proper.
Hayslett, H T
1991-01-01
Statistics covers the basic principles of Statistics. The book starts by tackling the importance and the two kinds of statistics; the presentation of sample data; the definition, illustration and explanation of several measures of location; and the measures of variation. The text then discusses elementary probability, the normal distribution and the normal approximation to the binomial. Testing of statistical hypotheses and tests of hypotheses about the theoretical proportion of successes in a binomial population and about the theoretical mean of a normal population are explained. The text the
Statistical models for nuclear decay from evaporation to vaporization
Cole, A J
2000-01-01
Elements of equilibrium statistical mechanics: Introduction. Microstates and macrostates. Sub-systems and convolution. The Boltzmann distribution. Statistical mechanics and thermodynamics. The grand canonical ensemble. Equations of state for ideal and real gases. Pseudo-equilibrium. Statistical models of nuclear decay. Nuclear physics background: Introduction. Elements of the theory of nuclear reactions. Quantum mechanical description of scattering from a potential. Decay rates and widths. Level and state densities in atomic nuclei. Angular momentum in quantum mechanics. History of statistical
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
Yang, Shuyu; Mitra, Sunanda; Corona, Enrique; Nutter, Brian; Lee, DJ
2003-12-01
Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.
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...
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.
Functional summary statistics for the Johnson-Mehl model
DEFF Research Database (Denmark)
Møller, Jesper; Ghorbani, Mohammad
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....
Statistical modeling and recognition of surgical workflow.
Padoy, Nicolas; Blum, Tobias; Ahmadi, Seyed-Ahmad; Feussner, Hubertus; Berger, Marie-Odile; Navab, Nassir
2012-04-01
In this paper, we contribute to the development of context-aware operating rooms by introducing a novel approach to modeling and monitoring the workflow of surgical interventions. We first propose a new representation of interventions in terms of multidimensional time-series formed by synchronized signals acquired over time. We then introduce methods based on Dynamic Time Warping and Hidden Markov Models to analyze and process this data. This results in workflow models combining low-level signals with high-level information such as predefined phases, which can be used to detect actions and trigger an event. Two methods are presented to train these models, using either fully or partially labeled training surgeries. Results are given based on tool usage recordings from sixteen laparoscopic cholecystectomies performed by several surgeons.
Statistical modelling of fine red wine production
María Rosa Castro; Marcelo Eduardo Echegaray; Rosa Ana Rodríguez; Stella Maris Udaquiola
2010-01-01
Producing wine is a very important economic activity in the province of San Juan in Argentina; it is therefore most important to predict production regarding the quantity of raw material needed. This work was aimed at obtaining a model relating kilograms of crushed grape to the litres of wine so produced. Such model will be used for predicting precise future values and confidence intervals for determined quantities of crushed grapes. Data from a vineyard in the province of San Juan was ...
On the Logical Development of Statistical Models.
1983-12-01
parameters t2 . Type I models include scalar and vectorial probability distributions. Usually, the noise has an expected value equal to zero, so that...qualitative variables. As might be expected, the vectorial representation of all these types of models lagged behind the scaler forms. The first...1978). "Modelos con parametros variables en el analisis de series temporales" Questiio, 4, 2, 75-87. [25] Seal, H. L. (1967). "The historical
Directory of Open Access Journals (Sweden)
Wenchao Cui
2013-01-01
Full Text Available 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.
Book review: Statistical Analysis and Modelling of Spatial Point Patterns
DEFF Research Database (Denmark)
Møller, Jesper
2009-01-01
Statistical Analysis and Modelling of Spatial Point Patterns by J. Illian, A. Penttinen, H. Stoyan and D. Stoyan. Wiley (2008), ISBN 9780470014912......Statistical Analysis and Modelling of Spatial Point Patterns by J. Illian, A. Penttinen, H. Stoyan and D. Stoyan. Wiley (2008), ISBN 9780470014912...
A Probabilistic Model for Reducing Medication Errors
Nguyen, Phung Anh; Syed-Abdul, Shabbir; Iqbal, Usman; Hsu, Min-Huei; Huang, Chen-Ling; Li, Hsien-Chang; Clinciu, Daniel Livius; Jian, Wen-Shan; Li, Yu-Chuan Jack
2013-01-01
Background Medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases. Methods and Finding(s) Association rules of mining techniques are utilized for 103.5 million prescriptions from Taiwan’s National Health Insurance database. The dataset included 204.5 million diagnoses with ICD9-CM codes and 347.7 million medications by using ATC codes. Disease-Medication (DM) and Medication-Medication (MM) associations were computed by their co-occurrence and associations’ strength were measured by the interestingness or lift values which were being referred as Q values. The DMQs and MMQs were used to develop the AOP model to predict the appropriateness of a given prescription. Validation of this model was done by comparing the results of evaluation performed by the AOP model and verified by human experts. The results showed 96% accuracy for appropriate and 45% accuracy for inappropriate prescriptions, with a sensitivity and specificity of 75.9% and 89.5%, respectively. Conclusions We successfully developed the AOP model as an efficient tool for automatic identification of uncommon or rare associations between disease-medication and medication-medication in prescriptions. The AOP model helps to reduce medication errors by alerting physicians, improving the patients’ safety and the overall quality of care. PMID:24312659
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.
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.
A statistical model of facial attractiveness.
Said, Christopher P; Todorov, Alexander
2011-09-01
Previous research has identified facial averageness and sexual dimorphism as important factors in facial attractiveness. The averageness and sexual dimorphism accounts provide important first steps in understanding what makes faces attractive, and should be valued for their parsimony. However, we show that they explain relatively little of the variance in facial attractiveness, particularly for male faces. As an alternative to these accounts, we built a regression model that defines attractiveness as a function of a face's position in a multidimensional face space. The model provides much more predictive power than the averageness and sexual dimorphism accounts and reveals previously unreported components of attractiveness. The model shows that averageness is attractive in some dimensions but not in others and resolves previous contradictory reports about the effects of sexual dimorphism on the attractiveness of male faces.
Fluctuations of offshore wind generation: Statistical modelling
DEFF Research Database (Denmark)
Pinson, Pierre; Christensen, Lasse E.A.; Madsen, Henrik
2007-01-01
The magnitude of power fluctuations at large offshore wind farms has a significant impact on the control and management strategies of their power output. If focusing on the minute scale, one observes successive periods with smaller and larger power fluctuations. It seems that different regimes...... production averaged at a 1, 5, and 10-minute rate. The exercise consists in one-step ahead forecasting of these time-series with the various regime-switching models. It is shown that the MSAR model, for which the succession of regimes is represented by a hidden Markov chain, significantly outperforms...
Statistical modelling of traffic safety development
DEFF Research Database (Denmark)
Christens, Peter
2004-01-01
: - Statistisk modellering af trafik uheld, Trafikdage på Ålborg Univeristet, 2001. - Sociale karakteristika hos trafikofre, Danish Transport Research Institute, 2001. - Models for traffic accidents, FERSI Young Researchers' Seminar, 2001. - Evaluation of the Danish Automatic Mobile Speed Camera Project...... 2000 dræbte trafikuheld over 40.000 i EU og skadede over 1.7 millioner. I Danmark i 2001 var der 6861 politirapporteret trafikuheld med tilskadekomst. De resulterede i 4519 lettere tilskadekomne, 3946 alvorligt tilskadekomne og 431 dræbte. Det generelle formål med dette forskningsarbejde er at forbedre...
Exponential order statistic models of software reliability growth
Miller, D. R.
1986-01-01
Failure times of a software reliability growth process are modeled as order statistics of independent, nonidentically distributed exponential random variables. The Jelinsky-Moranda, Goel-Okumoto, Littlewood, Musa-Okumoto Logarithmic, and Power Law models are all special cases of Exponential Order Statistic Models, but there are many additional examples also. Various characterizations, properties and examples of this class of models are developed and presented.
Statistical Modeling of Large-Scale Scientific Simulation Data
Energy Technology Data Exchange (ETDEWEB)
Eliassi-Rad, T; Baldwin, C; Abdulla, G; Critchlow, T
2003-11-15
With the advent of massively parallel computer systems, scientists are now able to simulate complex phenomena (e.g., explosions of a stars). Such scientific simulations typically generate large-scale data sets over the spatio-temporal space. Unfortunately, the sheer sizes of the generated data sets make efficient exploration of them impossible. Constructing queriable statistical models is an essential step in helping scientists glean new insight from their computer simulations. We define queriable statistical models to be descriptive statistics that (1) summarize and describe the data within a user-defined modeling error, and (2) are able to answer complex range-based queries over the spatiotemporal dimensions. In this chapter, we describe systems that build queriable statistical models for large-scale scientific simulation data sets. In particular, we present our Ad-hoc Queries for Simulation (AQSim) infrastructure, which reduces the data storage requirements and query access times by (1) creating and storing queriable statistical models of the data at multiple resolutions, and (2) evaluating queries on these models of the data instead of the entire data set. Within AQSim, we focus on three simple but effective statistical modeling techniques. AQSim's first modeling technique (called univariate mean modeler) computes the ''true'' (unbiased) mean of systematic partitions of the data. AQSim's second statistical modeling technique (called univariate goodness-of-fit modeler) uses the Andersen-Darling goodness-of-fit method on systematic partitions of the data. Finally, AQSim's third statistical modeling technique (called multivariate clusterer) utilizes the cosine similarity measure to cluster the data into similar groups. Our experimental evaluations on several scientific simulation data sets illustrate the value of using these statistical models on large-scale simulation data sets.
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
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...
Statistical Model of the 3-D Braided Composites Strength
Institute of Scientific and Technical Information of China (English)
XIAO Laiyuan; ZUO Weiwei; CAI Ganwei; LIAO Daoxun
2007-01-01
Based on the statistical model for the tensile statistical strength of unidirectional composite materials and the stress analysis of 3-D braided composites, a new method is proposed to calculate the tensile statistical strength of the 3-D braided composites. With this method, the strength of 3-D braided composites can be calculated with very large accuracy, and the statistical parameters of 3-D braided composites can be determined. The numerical result shows that the tensile statistical strength of 3-D braided composites can be predicted using this method.
Eigenfunction statistics in the localized Anderson model
Killip, R
2006-01-01
We consider the localized region of the Anderson model and study the distribution of eigenfunctions simultaneously in space and energy. In a natural scaling limit, we prove convergence to a Poisson process. This provides a counterpoint to recent work, which proves repulsion of the localization centres in a subtly different regime.
Statistical modelling of fine red wine production
Directory of Open Access Journals (Sweden)
María Rosa Castro
2010-05-01
Full Text Available Producing wine is a very important economic activity in the province of San Juan in Argentina; it is therefore most important to predict production regarding the quantity of raw material needed. This work was aimed at obtaining a model relating kilograms of crushed grape to the litres of wine so produced. Such model will be used for predicting precise future values and confidence intervals for determined quantities of crushed grapes. Data from a vineyard in the province of San Juan was thus used in this work. The sampling coefficient of correlation was calculated and a dispersion diagram was then constructed; this indicated a li- neal relationship between the litres of wine obtained and the kilograms of crushed grape. Two lineal models were then adopted and variance analysis was carried out because the data came from normal populations having the same variance. The most appropriate model was obtained from this analysis; it was validated with experimental values, a good approach being obtained.
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…
Probing NWP model deficiencies by statistical postprocessing
DEFF Research Database (Denmark)
Rosgaard, Martin Haubjerg; Nielsen, Henrik Aalborg; Nielsen, Torben S.
2016-01-01
numerical weather prediction (NWP) model generating global weather forecasts four times daily, with numerous users worldwide. The analysis is based on two years of hourly wind speed time series measured at three locations; offshore, in coastal and flat terrain, and inland in complex topography, respectively...
Network Data: Statistical Theory and New Models
2016-02-17
Using AERONET DRAGON Campaign Data, IEEE Transactions on Geoscience and Remote Sensing, (08 2015): 0. doi: 10.1109/TGRS.2015.2395722 Geoffrey...are not viable, i.e. the fruit fly dies after the knock-out of the gene. Further examination of the ftz stained embryos indicates that the lack of...our approach for spatial gene expression analysis for early stage fruit fly embryos, we are in a process to extend it to model later stage gene
Guocheng Yang; Meiling Li; Leiting Chen; Jie Yu
2015-01-01
We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are devel...
Behavioral and Statistical Models of Educational Inequality
DEFF Research Database (Denmark)
Holm, Anders; Breen, Richard
2016-01-01
This article addresses the question of how students and their families make educational decisions. We describe three types of behavioral model that might underlie decision-making, and we show that they have consequences for what decisions are made. Our study, thus, has policy implications if we...... wish to encourage students and their families to make better educational choices. We also establish the conditions under which empirical analysis can distinguish between the three sorts of decision-making, and we illustrate our arguments using data from the National Educational Longitudinal Study....
Behavioral and Statistical Models of Educational Inequality
DEFF Research Database (Denmark)
Holm, Anders; Breen, Richard
2016-01-01
This paper addresses the question of how students and their families make educational decisions. We describe three types of behavioral model that might underlie decision-making and we show that they have consequences for what decisions are made. Our study thus has policy implications if we wish...... to encourage students and their families to make better educational choices. We also establish the conditions under which empirical analysis can distinguish between the three sorts of decision-making and we illustrate our arguments using data from the National Educational Longitudinal Study....
Directory of Open Access Journals (Sweden)
Staniforth David
2008-05-01
Full Text Available Abstract Background Statistics is relevant to students and practitioners in medicine and health sciences and is increasingly taught as part of the medical curriculum. However, it is common for students to dislike and under-perform in statistics. We sought to address these issues by redesigning the way that statistics is taught. Methods The project brought together a statistician, clinician and educational experts to re-conceptualize the syllabus, and focused on developing different methods of delivery. New teaching materials, including videos, animations and contextualized workbooks were designed and produced, placing greater emphasis on applying statistics and interpreting data. Results Two cohorts of students were evaluated, one with old style and one with new style teaching. Both were similar with respect to age, gender and previous level of statistics. Students who were taught using the new approach could better define the key concepts of p-value and confidence interval (p Conclusion Using a variety of media, and placing emphasis on interpretation can help make teaching, learning and understanding of statistics more people-centred and relevant, resulting in better outcomes for students.
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...
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
Isoscaling in Statistical Sequential Decay Model
Institute of Scientific and Technical Information of China (English)
TIAN Wen-Dong; SU Qian-Min; WANG Hong-Wei; WANG Kun; YAN Ting-ZHi; MA Yu-Gang; CAI Xiang-Zhou; FANG De-Qing; GUO Wei; MA Chun-Wang; LIU Gui-Hua; SHEN Wen-Qing; SHI Yu
2007-01-01
A sequential decay model is used to study isoscaling, I.e. The factorization of the isotope ratios from sources of different isospins and sizes over a broad range of excitation energies, into fugacity terms of proton and neutron number, R21(N, Z) = Y2(N, Z)/Y1(N, Z) = Cexp(αN +βZ). It is found that the isoscaling parameters α and β have a strong dependence on the isospin difference of equilibrated source and excitation energy, no significant influence of the source size on α andβ has been observed. It is found that α and β decrease with the excitation energy and are linear functions of 1/T and △(Z/A)2 or △(N/A)2 of the sources. Symmetry energy coefficient Csym is constrained from the relationship of α and source △(Z/A)2, β and source △(N/A)2.
Process Model Construction and Optimization Using Statistical Experimental Design,
1988-04-01
Memo No. 88-442 ~LECTE March 1988 31988 %,.. MvAY 1 98 0) PROCESS MODEL CONSTRUCTION AND OPTIMIZATION USING STATISTICAL EXPERIMENTAL DESIGN Emmanuel...Sachs and George Prueger Abstract A methodology is presented for the construction of process models by the combination of physically based mechanistic...253-8138. .% I " Process Model Construction and Optimization Using Statistical Experimental Design" by Emanuel Sachs Assistant Professor and George
Daisy Models Semi-Poisson statistics and beyond
Hernández-Saldaña, H; Seligman, T H
1999-01-01
Semi-Poisson statistics are shown to be obtained by removing every other number from a random sequence. Retaining every (r+1)th level we obtain a family of secuences which we call daisy models. Their statistical properties coincide with those of Bogomolny's nearest-neighbour interaction Coulomb gas if the inverse temperature coincides with the integer r. In particular the case r=2 reproduces closely the statistics of quasi-optimal solutions of the traveling salesman problem.
Development of statistical models for data analysis
Energy Technology Data Exchange (ETDEWEB)
Downham, D.Y.
2000-07-01
Incidents that cause, or could cause, injury to personnel, and that satisfy specific criteria, are reported to the Offshore Safety Division (OSD) of the Health and Safety Executive (HSE). The underlying purpose of this report is to improve ways of quantifying risk, a recommendation in Lord Cullen's report into the Piper Alpha disaster. Records of injuries and hydrocarbon releases from 1 January, 1991, to 31 March 1996, are analysed, because the reporting of incidents was standardised after 1990. Models are identified for risk assessment and some are applied. The appropriate analyses of one or two factors (or variables) are tests of uniformity or of independence. Radar graphs are used to represent some temporal variables. Cusums are applied for the analysis of incident frequencies over time, and could be applied for regular monitoring. Log-linear models for Poisson-distributed data are identified as being suitable for identifying 'non-random' combinations of more than two factors. Some questions cannot be addressed with the available data: for example, more data are needed to assess the risk of injury per employee in a time interval. If the questions are considered sufficiently important, resources could be assigned to obtain the data. Some of the main results from the analyses are as follows: the cusum analyses identified a change-point at the end of July 1993, when the reported number of injuries reduced by 40%. Injuries were more likely to occur between 8am and 12am or between 2pm and 5pm than at other times: between 2pm and 3pm the number of injuries was almost twice the average and was more than three fold the smallest. No seasonal effects in the numbers of injuries were identified. Three-day injuries occurred more frequently on the 5th, 6th and 7th days into a tour of duty than on other days. Three-day injuries occurred less frequently on the 13th and 14th days of a tour of duty. An injury classified as 'lifting or craning' was
Fingernail Injuries and NASA's Integrated Medical Model
Kerstman, Eric; Butler, Doug
2008-01-01
The goal of space medicine is to optimize both crew health and performance. Currently, expert opinion is primarily relied upon for decision-making regarding medical equipment and supplies flown in space. Evidence-based decisions are preferred due to mass and volume limitations and the expense of space flight. The Integrated Medical Model (IMM) is an attempt to move us in that direction!
Mixed deterministic statistical modelling of regional ozone air pollution
Kalenderski, Stoitchko Dimitrov
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..
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.
The Importance of Statistical Modeling in Data Analysis and Inference
Rollins, Derrick, Sr.
2017-01-01
Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound…
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.
Powerline Communications Channel Modelling Methodology Based on Statistical Features
Tan, Bo
2012-01-01
This paper proposes a new channel modelling method for powerline communications networks based on the multipath profile in the time domain. The new channel model is developed to be applied in a range of Powerline Communications (PLC) research topics such as impulse noise modelling, deployment and coverage studies, and communications theory analysis. To develop the methodology, channels are categorised according to their propagation distance and power delay profile. The statistical multipath parameters such as path arrival time, magnitude and interval for each category are analyzed to build the model. Each generated channel based on the proposed statistical model represents a different realisation of a PLC network. Simulation results in similar the time and frequency domains show that the proposed statistical modelling method, which integrates the impact of network topology presents the PLC channel features as the underlying transmission line theory model. Furthermore, two potential application scenarios are d...
Isospin dependence of nuclear multifragmentation in statistical model
Institute of Scientific and Technical Information of China (English)
张蕾; 谢东珠; 张艳萍; 高远
2011-01-01
The evolution of nuclear disintegration mechanisms with increasing excitation energy, from compound nucleus to multifragmentation, has been studied by using the Statistical Multifragmentation Model （SMM） within a micro-canonical ensemble. We discuss the o
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
Statistical modeling of a considering work-piece
Directory of Open Access Journals (Sweden)
Cornelia Victoria Anghel
2008-10-01
Full Text Available In this article are presented the stochastic predictive models for controlling properly the independent variables of the drilling operation a combined approach of statistical design and Response Surface Methodology (RSM.
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.
A no extensive statistical model for the nucleon structure function
Trevisan, Luis A.; Mirez, Carlos
2013-03-01
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.
Model of risk assessment under ballistic statistical tests
Gabrovski, Ivan; Karakaneva, Juliana
The material presents the application of a mathematical method for risk assessment under statistical determination of the ballistic limits of the protection equipment. The authors have implemented a mathematical model based on Pierson's criteria. The software accomplishment of the model allows to evaluate the V50 indicator and to assess the statistical hypothesis' reliability. The results supply the specialists with information about the interval valuations of the probability determined during the testing process.
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
Yang, Guocheng; Li, Meiling; Chen, Leiting; Yu, Jie
2015-01-01
We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.
Directory of Open Access Journals (Sweden)
Guocheng Yang
2015-01-01
Full Text Available We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD, as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.
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...
Electron impact ionization of tungsten ions in a statistical model
Demura, A. V.; Kadomtsev, M. B.; Lisitsa, V. S.; Shurygin, V. A.
2015-01-01
The statistical model for calculations of the electron impact ionization cross sections of multielectron ions is developed for the first time. The model is based on the idea of collective excitations of atomic electrons with the local plasma frequency, while the Thomas-Fermi model is used for atomic electrons density distribution. The electron impact ionization cross sections and related ionization rates of tungsten ions from W+ up to W63+ are calculated and then compared with the vast collection of modern experimental and modeling results. The reasonable correspondence between experimental and theoretical data demonstrates the universal nature of statistical approach to the description of atomic processes in multielectron systems.
Xu, Peng; Yao, Dezhong; Luo, Fen
2005-08-01
The registration method based on mutual information is currently a popular technique for the medical image registration, but the computation for the mutual information is complex and the registration speed is slow. In engineering process, a subsampling technique is taken to accelerate the registration speed at the cost of registration accuracy. In this paper a new method based on statistics sample theory is developed, which has both a higher speed and a higher accuracy as compared with the normal subsampling method, and the simulation results confirm the validity of the new method.
Towards Statistical Trust Computation for Medical Smartphone Networks Based on Behavioral Profiling
DEFF Research Database (Denmark)
Meng, Weizhi; Au, Man Ho
2017-01-01
is woefully inadequate. Although MSNs are mostly internally used, they still can leak sensitive information under insider attacks. In this case, there is a need to evaluate a node’s trustworthiness in MSNs based on the network characteristics. In this paper, we focus on MSNs and propose a statistical trust-based...... intrusion detection mechanism to detect malicious nodes in terms of behavioral profiling (e.g., camera usage, visited websites, etc.). Experimental results indicate that our proposed mechanism is feasible and promising in detecting malicious nodes under medical environments....
Thakur, A; Anand, R S
2007-01-01
This article discusses an adaptive filtering technique for reducing speckle using second order statistics of the speckle pattern in ultrasound medical images. Several region-based adaptive filter techniques have been developed for speckle noise suppression, but there are no specific criteria for selecting the region growing size in the post processing of the filter. The size appropriate for one local region may not be appropriate for other regions. Selection of the correct region size involves a trade-off between speckle reduction and edge preservation. Generally, a large region size is used to smooth speckle and a small size to preserve the edges into an image. In this paper, a smoothing procedure combines the first order statistics of speckle for the homogeneity test and second order statistics for selection of filters and desired region growth. Grey level co-occurrence matrix (GLCM) is calculated for every region during the region contraction and region growing for second order statistics. Further, these GLCM features determine the appropriate filter for the region smoothing. The performance of this approach is compared with the aggressive region-growing filter (ARGF) using edge preservation and speckle reduction tests. The processed image results show that the proposed method effectively reduces speckle noise and preserves edge details.
An Order Statistics Approach to the Halo Model for Galaxies
Paul, Niladri; Paranjape, Aseem; Sheth, Ravi K.
2017-01-01
We use the Halo Model to explore the implications of assuming that galaxy luminosities in groups are randomly drawn from an underlying luminosity function. We show that even the simplest of such order statistics models - one in which this luminosity function p(L) is universal - naturally produces a number of features associated with previous analyses based on the `central plus Poisson satellites' hypothesis. These include the monotonic relation of mean central luminosity with halo mass, the Lognormal distribution around this mean, and the tight relation between the central and satellite mass scales. In stark contrast to observations of galaxy clustering, however, this model predicts no luminosity dependence of large scale clustering. We then show that an extended version of this model, based on the order statistics of a halo mass dependent luminosity function p(L|m), is in much better agreement with the clustering data as well as satellite luminosities, but systematically under-predicts central luminosities. This brings into focus the idea that central galaxies constitute a distinct population that is affected by different physical processes than are the satellites. We model this physical difference as a statistical brightening of the central luminosities, over and above the order statistics prediction. The magnitude gap between the brightest and second brightest group galaxy is predicted as a by-product, and is also in good agreement with observations. We propose that this order statistics framework provides a useful language in which to compare the Halo Model for galaxies with more physically motivated galaxy formation models.
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.
Medical School Attrition-Beyond the Statistics A Ten Year Retrospective Study
2013-01-01
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 students combined. North American students had a higher dropout rate than Irish and EU students; RR = 2.68 (1.09 to 6.58;p = 0.027) but this was not significant when transfers were excluded (RR = 1.32(0.38, 4.62);p = 0.75). Male students were more likely to dropout than females (RR 1.70, .93 to 3.11) but this was not significant (p = 0.079). 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
Equilibrium Statistical-Thermal Models in High-Energy Physics
Tawfik, Abdel Nasser
2014-01-01
We review some recent highlights from the applications of statistical-thermal models to different experimental measurements and lattice QCD thermodynamics, that have been made during the last decade. We start with a short review of the historical milestones on the path of constructing statistical-thermal models for heavy-ion physics. We discovered that Heinz Koppe formulated in 1948 an almost complete recipe for the statistical-thermal models. In 1950, Enrico Fermi generalized this statistical approach, in which he started with a general cross-section formula and inserted into it simplifying assumptions about the matrix element of the interaction process that likely reflects many features of the high-energy reactions dominated by density in the phase space of final states. In 1964, Hagedorn systematically analysed the high-energy phenomena using all tools of statistical physics and introduced the concept of limiting temperature based on the statistical bootstrap model. It turns to be quite often that many-par...
Modelling empathy in medical and nursing education.
Malpas, Phillipa J; Corbett, Andrea
2012-03-30
Medical and nursing student numbers are expected to increase significantly in NZ over the next few years. The ethical, and professional and clinical skills' training of trainee health practitioners is a central and crucial component in medical and nursing education and is underpinned by a strong commitment to improve patient health and well being. In this discussion we reflect on the virtue of empathy and the importance of role modelling in the education of nurses and doctors. We endorse the claim that as medical educators, how and what we teach matters.
Jamsen, Kris M; Ilomäki, Jenni; Hilmer, Sarah N; Jokanovic, Natali; Tan, Edwin C K; Bell, J Simon
2016-01-01
There is increasing awareness that medications can contribute to cognitive decline. Prospective cohort studies are rich sources of clinical data. However, investigating the contribution of medications to cognitive decline is challenging because both medication exposure and cognitive impairment can be associated with attrition of study participants, and medication exposure status may change over time. The objective of this review was to investigate the statistical methods in prospective cohort studies assessing the effect of medications on cognition in older people. A systematic literature search was conducted to identify prospective cohort studies of at least 12 months duration that investigated the effect of common medications or medication classes (anticholinergics, antihistamines, hypnotics, sedatives, opioids, statins, estrogens, testosterone, antipsychotics, anticonvulsants, antidepressants, anxiolytics, antiparkinson agents and bronchodilators) on cognition in people aged 65 years and older. Data extraction was performed independently by two investigators. A descriptive analysis of the statistical methods was performed. A total of 44 articles were included in the review. The most common statistical methods were logistic regression (24.6% of all reported methods), Cox proportional hazards regression (22.8%), linear mixed-effects models (21.1%) and multiple linear regression (14.0%). The use of advanced techniques, most notably linear mixed-effects models, increased over time. Only 6 articles (13.6%) reported methods for addressing missing data. A variety of statistical methods have been used for investigating the effect of medications on cognition in older people. While advanced techniques that are appropriate for the analysis of longitudinal data, most notably linear mixed-effects models, have increasingly been employed in recent years, there is an opportunity to implement alternative techniques in future studies that could address key research questions
Advanced interaction techniques for medical models
Monclús, Eva
2014-01-01
Advances in Medical Visualization allows the analysis of anatomical structures with the use of 3D models reconstructed from a stack of intensity-based images acquired through different techniques, being Computerized Tomographic (CT) modality one of the most common. A general medical volume graphics application usually includes an exploration task which is sometimes preceded by an analysis process where the anatomical structures of interest are first identified. ...
Statistical Model and the mesonic-baryonic transition region
Oeschler, H.; Redlich, K.; Wheaton, S.
2009-01-01
The statistical model assuming chemical equilibriumand local strangeness conservation describes most of the observed features of strange particle production from SIS up to RHIC. Deviations are found as the maximum in the measured K+/pi+ ratio is much sharper than in the model calculations. At the incident energy of the maximum, the statistical model shows that freeze out changes regime from one being dominated by baryons at the lower energies toward one being dominated by mesons. It will be shown how deviations from the usual freeze-out curve influence the various particle ratios. Furthermore, other observables exhibit also changes just in this energy regime.
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
A statistical model for the excitation of cavities through apertures
Gradoni, Gabriele; Anlage, Steven M; Ott, Edward
2015-01-01
In this paper, a statistical model for the coupling of electromagnetic radiation into enclosures through apertures is presented. The model gives a unified picture bridging deterministic theories of aperture radiation, and statistical models necessary for capturing the properties of irregular shaped enclosures. A Monte Carlo technique based on random matrix theory is used to predict and study the power transmitted through the aperture into the enclosure. Universal behavior of the net power entering the aperture is found. Results are of interest for predicting the coupling of external radiation through openings in irregular enclosures and reverberation chambers.
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.
Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics
Scheuerer, Michael
2013-01-01
Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the Germa...
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.
What is the meaning of the statistical hadronization model?
Becattini, F
2005-01-01
The statistical model of hadronization succeeds in reproducing particle abundances and transverse momentum spectra in high energy collisions of elementary particles as well as of heavy ions. Despite its apparent success, the interpretation of these results is controversial and the validity of the approach very often questioned. In this paper, we would like to summarize the whole issue by first outlining a basic formulation of the model and then comment on the main criticisms and different kinds of interpretations, with special emphasis on the so-called "phase space dominance". While the ultimate answer to the question why the statistical model works should certainly be pursued, we stress that it is a priority to confirm or disprove the fundamental scheme of the statistical model by performing some detailed tests on the rates of exclusive channels at lower energy.
In all likelihood statistical modelling and inference using likelihood
Pawitan, Yudi
2001-01-01
Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from asimile comparison of two accident rates, to complex studies that require generalised linear or semiparametric mode
Binary and Ternary Fission Within the Statistical Model
Adamian, Gurgen G.; Andreev, Alexander V.; Antonenko, Nikolai V.; Scheid, Werner
The binary and ternary nuclear fission are treated within the statistical model. At the scission point we calculate the potentials as functions of the deformations of the fragments in the dinuclear model. The potentials give the mass and charge distributions of the fission fragments. The ternary fission is assumed to occur during the binary fission.
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...
Statistical model of the classification of shale in a hydrocyclone
Energy Technology Data Exchange (ETDEWEB)
Lopachenok, L.V.; Punin, A.E.; Belyanin, Yu.I.; Proskuryakov, V.A.
1977-10-01
The mathematical model obtained by experimental and statistical methods for the classification of shale in a hydrocyclone is adequate for a real industrial-scale process, as indicated by the statistical analysis carried out for it, and together with the material-balance relationships it permits the calculation of the engineering parameters for any classification conditions within the region of the factor space investigated, as well as the search for the optimum conditions for the industrial realization of the process.
General Linear Models: An Integrated Approach to Statistics
Andrew Faulkner; Sylvain Chartier
2008-01-01
Generally, in psychology, the various statistical analyses are taught independently from each other. As a consequence, students struggle to learn new statistical analyses, in contexts that differ from their textbooks. This paper gives a short introduction to the general linear model (GLM), in which it is showed that ANOVA (one-way, factorial, repeated measure and analysis of covariance) is simply a multiple correlation/regression analysis (MCRA). Generalizations to other cases, such as multiv...
Stang, Andreas; Deckert, Markus; Poole, Charles; Rothman, Kenneth J
2017-01-01
Since its introduction in the twentieth century, null hypothesis significance testing (NHST), a hybrid of significance testing (ST) advocated by Fisher and null hypothesis testing (NHT) developed by Neyman and Pearson, has become widely adopted but has also been a source of debate. The principal alternative to such testing is estimation with point estimates and confidence intervals (CI). Our aim was to estimate time trends in NHST, ST, NHT and CI reporting in abstracts of major medical and epidemiological journals. We reviewed 89,533 abstracts in five major medical journals and seven major epidemiological journals, 1975-2014, and estimated time trends in the proportions of abstracts containing statistical inference. In those abstracts, we estimated time trends in the proportions relying on NHST and its major variants, ST and NHT, and in the proportions reporting CIs without explicit use of NHST (CI-only approach). The CI-only approach rose monotonically during the study period in the abstracts of all journals. In Epidemiology abstracts, as a result of the journal's editorial policy, the CI-only approach has always been the most common approach. In the other 11 journals, the NHST approach started out more common, but by 2014, this disparity had narrowed, disappeared or reversed in 9 of them. The exceptions were JAMA, New England Journal of Medicine, and Lancet abstracts, where the predominance of the NHST approach prevailed over time. In 2014, the CI-only approach is as popular as the NHST approach in the abstracts of 4 of the epidemiology journals: the American Journal of Epidemiology (48%), the Annals of Epidemiology (55%), Epidemiology (79%) and the International Journal of Epidemiology (52%). The reporting of CIs without explicitly interpreting them as statistical tests is becoming more common in abstracts, particularly in epidemiology journals. Although NHST is becoming less popular in abstracts of most epidemiology journals studied and some widely read medical
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).
Structural Characterization and Statistical-Mechanical Model of Epidermal Patterns.
Chen, Duyu; Aw, Wen Yih; Devenport, Danelle; Torquato, Salvatore
2016-12-06
In proliferating epithelia of mammalian skin, cells of irregular polygon-like shapes pack into complex, nearly flat two-dimensional structures that are pliable to deformations. In this work, we employ various sensitive correlation functions to quantitatively characterize structural features of evolving packings of epithelial cells across length scales in mouse skin. We find that the pair statistics in direct space (correlation function) and Fourier space (structure factor) of the cell centroids in the early stages of embryonic development show structural directional dependence (statistical anisotropy), which is a reflection of the fact that cells are stretched, which promotes uniaxial growth along the epithelial plane. In the late stages, the patterns tend toward statistically isotropic states, as cells attain global polarization and epidermal growth shifts to produce the skin's outer stratified layers. We construct a minimalist four-component statistical-mechanical model involving effective isotropic pair interactions consisting of hard-core repulsion and extra short-range soft-core repulsion beyond the hard core, whose length scale is roughly the same as the hard core. The model parameters are optimized to match the sample pair statistics in both direct and Fourier spaces. By doing this, the parameters are biologically constrained. In contrast with many vertex-based models, our statistical-mechanical model does not explicitly incorporate information about the cell shapes and interfacial energy between cells; nonetheless, our model predicts essentially the same polygonal shape distribution and size disparity of cells found in experiments, as measured by Voronoi statistics. Moreover, our simulated equilibrium liquid-like configurations are able to match other nontrivial unconstrained statistics, which is a testament to the power and novelty of the model. The array of structural descriptors that we deploy enable us to distinguish between normal, mechanically
Statistical Design Model (SDM) of satellite thermal control subsystem
Mirshams, Mehran; Zabihian, Ehsan; Aarabi Chamalishahi, Mahdi
2016-07-01
Satellites thermal control, is a satellite subsystem that its main task is keeping the satellite components at its own survival and activity temperatures. Ability of satellite thermal control plays a key role in satisfying satellite's operational requirements and designing this subsystem is a part of satellite design. In the other hand due to the lack of information provided by companies and designers still doesn't have a specific design process while it is one of the fundamental subsystems. The aim of this paper, is to identify and extract statistical design models of spacecraft thermal control subsystem by using SDM design method. This method analyses statistical data with a particular procedure. To implement SDM method, a complete database is required. Therefore, we first collect spacecraft data and create a database, and then we extract statistical graphs using Microsoft Excel, from which we further extract mathematical models. Inputs parameters of the method are mass, mission, and life time of the satellite. For this purpose at first thermal control subsystem has been introduced and hardware using in the this subsystem and its variants has been investigated. In the next part different statistical models has been mentioned and a brief compare will be between them. Finally, this paper particular statistical model is extracted from collected statistical data. Process of testing the accuracy and verifying the method use a case study. Which by the comparisons between the specifications of thermal control subsystem of a fabricated satellite and the analyses results, the methodology in this paper was proved to be effective. Key Words: Thermal control subsystem design, Statistical design model (SDM), Satellite conceptual design, Thermal hardware
Aspects Regarding the Medical Data Processing - The Statistical Study of Malocclusions
Directory of Open Access Journals (Sweden)
Georgeta ZEGAN
2012-11-01
Full Text Available An important aspect in the analysis of medical data is represented by their statistical processing, which gives useful directions in finding the diagnosis and the most adequate treatment, especially when the amount of statistical data is very large and extended in time. We give an example in this paper, by presenting a statistical evaluation on a consignment of patients who have addressed the Dental Ambulatory for Children (Iaşi for orthodontic consulting over a period of 10 years (2000 – 2010. The study has been performed on 375 patients (157 boys and 218 girls, with ages between 4-24 years and malocclusions. The diagnosis was established by clinical and paraclinical exams (cast and radiological measurements. Both removable and fixed appliances were used in conducting the treatment. The patients in need of orthodontic treatment presented malocclusion I-st Class 63,2%, II-nd Class 28,3%, and III-rd Class 5,8%. The group and isolated malocclusion proportion varied on the basis of the clinical manifestations associated to the malocclusion class. Based on age, the patients were applied with prophylactic treatment 3%, interceptive treatment 5% and curative 92%. The Pearson correlations made on the sample of patients proved the existence of a directly proportional connection between the therapeutic results, the diagnosis of the malocclusions and the treatment chosen to be carried out. The results regarding the prevalence malocclusion types are comparable with those from the literature. The correlations that were carried out were based on medical reasoning. All these results are useful to depict the general characteristics of the Dental Ambulatory’s potential patients and, as a consequence, to establish easier the most accurate treatment.
Statistical Inference of Biometrical Genetic Model With Cultural Transmission.
Guo, Xiaobo; Ji, Tian; Wang, Xueqin; Zhang, Heping; Zhong, Shouqiang
2013-01-01
Twin and family studies establish the foundation for studying the genetic, environmental and cultural transmission effects for phenotypes. In this work, we make use of the well established statistical methods and theory for mixed models to assess cultural transmission in twin and family studies. Specifically, we address two critical yet poorly understood issues: the model identifiability in assessing cultural transmission for twin and family data and the biases in the estimates when sub-models are used. We apply our models and theory to two real data sets. A simulation is conducted to verify the bias in the estimates of genetic effects when the working model is a sub-model.
Eiselstein, Lawrence E.; Steffey, Duane; Nissan, Andrew; Corlett, Nigel; Dugnani, Roberto; Kus, Esra; Stewart, Sarah G.
2009-08-01
ASTM F 2129 test method nor the FDA provides any guidance as to what constitutes an acceptance criterion for the corrosion resistance of implantable medical devices. Neither provide any guidance on how many samples to test or how to handle censored data, i.e. datasets where there are only a few tests that breakdown. The development of both a statistically valid acceptance criterion for corrosion resistance and a method of evaluation would be of significant benefit to the medical device community. This study of 420 nitinol cyclic polarization tests, which builds on previous research that was presented at SMST 2007, investigates the effect of long-term exposure to simulated in vivo environments with differing degrees of aeration. This was accomplished by pre-exposing electropolished (EP) nitinol to phosphate buffered saline (PBS) at 37 °C that had been sparged with either ultra high purity nitrogen or laboratory air. Immersion times ranged from 1 h up to 30 days. A total of 290 EP samples were tested in order to obtain a reasonable number of samples with breakdown, i.e. pitted. In addition, a total of 130 mechanical polished (MP) samples were also analyzed. This data allow us to test our statistical model that was presented at SMST 2007. This model takes into account the probability of breakdown per unit of exposed surface area and, if breakdown occurs, predicts the probability that E b - E r is greater than some threshold value. Aerated PBS environments were found to have a large influence on the margin of safety against pitting in vivo. Statistical methods for treating highly right censored pitting data are presented.
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
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...... between the psychosocial work environment and sickness absence were used to illustrate the results. RESULTS: Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models. CONCLUSIONS...
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.
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.
Modern statistical models for forensic fingerprint examinations: a critical review.
Abraham, Joshua; Champod, Christophe; Lennard, Chris; Roux, Claude
2013-10-10
Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework. This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.
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...
Statistical Modeling for Wind-Temperature Meteorological Elements in Troposphere
Virtser, A; Golbraikh, E
2010-01-01
A comprehensive statistical model for vertical profiles of the horizontal wind and temperature throughout the troposphere is presented. The model is based on radiosonde measurements of wind and temperature during several years. The profiles measured under quite different atmospheric conditions exhibit qualitative similarity, and a proper choice of the reference scales for the wind, temperature and altitude levels allows to consider the measurement data as realizations of a random process with universal characteristics: means, the basic functions and parameters of standard distributions for transform coefficients of the Principal Component Analysis. The features of the atmospheric conditions are described by statistical characteristics of the wind-temperature ensemble of dimensional reference scales. The high effectiveness of the proposed approach is provided by a similarity of wind - temperature vertical profiles, which allow to carry out the statistical modeling in the low-dimension space of the dimensional ...
Sensitivity Analysis and Statistical Convergence of a Saltating Particle Model
Maldonado, S
2016-01-01
Saltation models provide considerable insight into near-bed sediment transport. This paper outlines a simple, efficient numerical model of stochastic saltation, which is validated against previously published experimental data on saltation in a channel of nearly horizontal bed. Convergence tests are systematically applied to ensure the model is free from statistical errors emanating from the number of particle hops considered. Two criteria for statistical convergence are derived; according to the first criterion, at least $10^3$ hops appear to be necessary for convergent results, whereas $10^4$ saltations seem to be the minimum required in order to achieve statistical convergence in accordance with the second criterion. Two empirical formulae for lift force are considered: one dependent on the slip (relative) velocity of the particle multiplied by the vertical gradient of the horizontal flow velocity component; the other dependent on the difference between the squares of the slip velocity components at the to...
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.
LETTER: Statistical physics of the Schelling model of segregation
Dall'Asta, L.; Castellano, C.; Marsili, M.
2008-07-01
We investigate the static and dynamic properties of a celebrated model of social segregation, providing a complete explanation of the mechanisms leading to segregation both in one- and two-dimensional systems. Standard statistical physics methods shed light on the rich phenomenology of this simple model, exhibiting static phase transitions typical of kinetic constrained models, non-trivial coarsening like in driven-particle systems and percolation-related phenomena.
Understanding and forecasting polar stratospheric variability with statistical models
Directory of Open Access Journals (Sweden)
C. Blume
2012-02-01
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 a 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, etc., to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. FEM-VARX and MLP even satisfactorily forecast the period from 2005 to 2011. However, internal variability remains that cannot be statistically forecasted, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a vortex breakdown in late January, early February 2012.
Semantic models in medical record data-bases.
Cerutti, S
1980-01-01
A great effort has been recently made in the area of data-base design in a number of application fields (banking, insurance, travel, etc.). Yet, it is the current experience of computer scientists in the medical field that medical record information-processing requires less rigid and more complete definition of data-base specifications for a much more heterogeneous set of data, for different users who have different aims. Hence, it is important to state that the data-base in the medical field ought to be a model of the environment for which it was created, rather than just a collection of data. New more powerful and more flexible data-base models are being now designed, particularly in the USA, where the current trend in medicine is to implement, in the same structure, the connection among more different and specific users and the data-base (for administrative aims, medical care control, treatments, statistical and epidemiological results, etc.). In such a way the single users are able to talk with the data-base without interfering with one another. The present paper outlines that this multi-purpose flexibility can be achieved by improving mainly the capabilities of the data-base model. This concept allows the creation of procedures of semantic integrity control which will certainly have in the future a dramatic impact on important management features, starting from data-quality checking and non-physiological state detections, as far as more medical-oriented procedures like drug interactions, record surveillance and medical care review. That is especially true when a large amount of data are to be processed and the classical hierarchical and network data models are no longer sufficient for developing satisfactory and reliable automatic procedures. In this regard, particular emphasis will be dedicated to the relational model and, at the highest level, to the same semantic data model.
Quantitative statistical assessment of conditional models for synthetic aperture radar.
DeVore, Michael D; O'Sullivan, Joseph A
2004-02-01
Many applications of object recognition in the presence of pose uncertainty rely on statistical models-conditioned on pose-for observations. The image statistics of three-dimensional (3-D) objects are often assumed to belong to a family of distributions with unknown model parameters that vary with one or more continuous-valued pose parameters. Many methods for statistical model assessment, for example the tests of Kolmogorov-Smirnov and K. Pearson, require that all model parameters be fully specified or that sample sizes be large. Assessing pose-dependent models from a finite number of observations over a variety of poses can violate these requirements. However, a large number of small samples, corresponding to unique combinations of object, pose, and pixel location, are often available. We develop methods for model testing which assume a large number of small samples and apply them to the comparison of three models for synthetic aperture radar images of 3-D objects with varying pose. Each model is directly related to the Gaussian distribution and is assessed both in terms of goodness-of-fit and underlying model assumptions, such as independence, known mean, and homoscedasticity. Test results are presented in terms of the functional relationship between a given significance level and the percentage of samples that wold fail a test at that level.
A statistical shape model of the human second cervical vertebra.
Clogenson, Marine; Duff, John M; Luethi, Marcel; Levivier, Marc; Meuli, Reto; Baur, Charles; Henein, Simon
2015-07-01
Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.
Calculation of precise firing statistics in a neural network model
Cho, Myoung Won
2017-08-01
A precise prediction of neural firing dynamics is requisite to understand the function of and the learning process in a biological neural network which works depending on exact spike timings. Basically, the prediction of firing statistics is a delicate manybody problem because the firing probability of a neuron at a time is determined by the summation over all effects from past firing states. A neural network model with the Feynman path integral formulation is recently introduced. In this paper, we present several methods to calculate firing statistics in the model. We apply the methods to some cases and compare the theoretical predictions with simulation results.
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.
BI for Medical Insurance Statistics%BI在医疗保险统计中的应用
Institute of Scientific and Technical Information of China (English)
沈弘; 郭莺; 沈斻珠; 张卫国; 阴忆青
2012-01-01
In order to improve the efficiency of hospital management, to get statistical data related to medical insurance in short time and under the premise of not disturbing the normal operation of service database, modelling data warehouse solution is adopted. The solution uses Microsoft BI as a technical platform to construct an example of the medical insurance data warehouse, and provide the hospital with medical insurance data that are multi-layer and from multi-perspective by using the platform of Microsoft Office SharePoint Server 2007. The functions of data warehousing, data mining and data analysis that the BI platform offers provide the hospital with detailed evidence for decision-making in medical insurance related operations, which enables the medical resources to be allocated in a more timely, effective and reasonable way.%为了提高医院管理效率,在不干扰业务数据库正常运行的前提下,快速获得相关统计数据而采用数据建模的方法,以微软商务智能(business intelligence,BI)作为技术平台,构建了医保(社会医疗保险)数据仓库实例,并通过微软SharePoint server展示平台,为医院提供多层次、多角度的医保数据.BI平台的数据仓库、数据挖掘、数据分析,为医保相关业务提供了翔实的决策依据,做到更及时、更有效、更合理地配置医疗资源.
National Research Council Canada - National Science Library
Yazhou Wu; Liang Zhou; Gaoming Li; Dali Yi; Xiaojiao Wu; Xiaoyu Liu; Yanqi Zhang; Ling Liu; Dong Yi
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...
Live sequence charts to model medical information
Directory of Open Access Journals (Sweden)
Aslakson Eric
2012-06-01
Full Text Available Abstract Background Medical records accumulate data concerning patient health and the natural history of disease progression. However, methods to mine information systematically in a form other than an electronic health record are not yet available. The purpose of this study was to develop an object modeling technique as a first step towards a formal database of medical records. Method Live Sequence Charts (LSC were used to formalize the narrative text obtained during a patient interview. LSCs utilize a visual scenario-based programming language to build object models. LSC extends the classical language of UML message sequence charts (MSC, predominantly through addition of modalities and providing executable semantics. Inter-object scenarios were defined to specify natural history event interactions and different scenarios in the narrative text. Result A simulated medical record was specified into LSC formalism by translating the text into an object model that comprised a set of entities and events. The entities described the participating components (i.e., doctor, patient and record and the events described the interactions between elements. A conceptual model is presented to illustrate the approach. An object model was generated from data extracted from an actual new patient interview, where the individual was eventually diagnosed as suffering from Chronic Fatigue Syndrome (CFS. This yielded a preliminary formal designated vocabulary for CFS development that provided a basis for future formalism of these records. Conclusions Translation of medical records into object models created the basis for a formal database of the patient narrative that temporally depicts the events preceding disease, the diagnosis and treatment approach. The LSCs object model of the medical narrative provided an intuitive, visual representation of the natural history of the patient’s disease.
Model observers in medical imaging research.
He, Xin; Park, Subok
2013-10-04
Model observers play an important role in the optimization and assessment of imaging devices. In this review paper, we first discuss the basic concepts of model observers, which include the mathematical foundations and psychophysical considerations in designing both optimal observers for optimizing imaging systems and anthropomorphic observers for modeling human observers. Second, we survey a few state-of-the-art computational techniques for estimating model observers and the principles of implementing these techniques. Finally, we review a few applications of model observers in medical imaging research.
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.
Schedulability of Herschel revisited using statistical model checking
DEFF Research Database (Denmark)
David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel
2015-01-01
Schedulability analysis is a main concern for several embedded applications due to their safety-critical nature. The classical method of response time analysis provides an efficient technique used in industrial practice. However, the method is based on conservative assumptions related to execution...... to obtain some guarantee on the (un)schedulability of the model even in the presence of undecidability. Two methods are considered: symbolic model checking and statistical model checking. Since the model uses stop-watches, the reachability problem becomes undecidable so we are using an over......-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...
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.
Medical image segmentation by MDP model
Lu, Yisu; Chen, Wufan
2011-11-01
MDP (Dirichlet Process Mixtures) model is applied to segment medical images in this paper. Segmentation can been automatically done without initializing segmentation class numbers. The MDP model segmentation algorithm is used to segment natural images and MR (Magnetic Resonance) images in the paper. To demonstrate the accuracy of the MDP model segmentation algorithm, many compared experiments, such as EM (Expectation Maximization) image segmentation algorithm, K-means image segmentation algorithm and MRF (Markov Field) image segmentation algorithm, have been done to segment medical MR images. All the methods are also analyzed quantitatively by using DSC (Dice Similarity Coefficients). The experiments results show that DSC of MDP model segmentation algorithm of all slices exceed 90%, which show that the proposed method is robust and accurate.
Correlation of admissions statistics to graduate student success in medical physics.
Burmeister, Jay; McSpadden, Erin; Rakowski, Joseph; Nalichowski, Adrian; Yudelev, Mark; Snyder, Michael
2014-01-06
The purpose of this work is to develop metrics for evaluation of medical physics graduate student performance, assess relationships between success and other quantifiable factors, and determine whether graduate student performance can be accurately predicted by admissions statistics. A cohort of 108 medical physics graduate students from a single institution were rated for performance after matriculation based on final scores in specific courses, first year graduate Grade Point Average (GPA), performance on the program exit exam, performance in oral review sessions, and faculty rating. Admissions statistics including matriculating program (MS vs. PhD); undergraduate degree type, GPA, and country; graduate degree; general and subject GRE scores; traditional vs. nontraditional status; and ranking by admissions committee were evaluated for potential correlation with the performance metrics. GRE verbal and quantitative scores were correlated with higher scores in the most difficult courses in the program and with the program exit exam; however, the GRE section most correlated with overall faculty rating was the analytical writing section. Students with undergraduate degrees in engineering had a higher faculty rating than those from other disciplines and faculty rating was strongly correlated with undergraduate country. Undergraduate GPA was not statistically correlated with any success metrics investigated in this study. However, the high degree of selection on GPA and quantitative GRE scores during the admissions process results in relatively narrow ranges for these quantities. As such, these results do not necessarily imply that one should not strongly consider traditional metrics, such as undergraduate GPA and quantitative GRE score, during the admissions process. They suggest that once applicants have been initially filtered by these metrics, additional selection should be performed via the other metrics shown here to be correlated with success. The parameters used
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.
Modelling geographical graduate job search using circular statistics
Faggian, Alessandra; Corcoran, Jonathan; McCann, Philip
2013-01-01
Theory suggests that the spatial patterns of migration flows are contingent both on individual human capital and underlying geographical structures. Here we demonstrate these features by using circular statistics in an econometric modelling framework applied to the flows of UK university graduates.
Interactive comparison of hypothesis tests for statistical model checking
de Boer, Pieter-Tjerk; Reijsbergen, D.P.; Scheinhardt, Willem R.W.
2015-01-01
We present a web-based interactive comparison of hypothesis tests as are used in statistical model checking, providing users and tool developers with more insight into their characteristics. Parameters can be modified easily and their influence is visualized in real time; an integrated simulation
Validation of Models : Statistical Techniques and Data Availability
Kleijnen, J.P.C.
1999-01-01
This paper shows which statistical techniques can be used to validate simulation models, depending on which real-life data are available. Concerning this availability three situations are distinguished (i) no data, (ii) only output data, and (iii) both input and output data. In case (i) - no real
Statistical Modeling for Radiation Hardness Assurance: Toward Bigger Data
Ladbury, R.; Campola, M. J.
2015-01-01
New approaches to statistical modeling in radiation hardness assurance are discussed. These approaches yield quantitative bounds on flight-part radiation performance even in the absence of conventional data sources. This allows the analyst to bound radiation risk at all stages and for all decisions in the RHA process. It also allows optimization of RHA procedures for the project's risk tolerance.
Nowcasting GDP Growth: statistical models versus professional analysts
J.M. de Winter (Jasper)
2016-01-01
markdownabstractThis thesis contains four chapters that cast new light on the ability of professional analysts and statistical models to assess economic growth in the current quarter (nowcast) and its development in the near future. This is not a trivial issue. An accurate assessment of the current
Hypersonic Vehicle Tracking Based on Improved Current Statistical Model
Directory of Open Access Journals (Sweden)
He Guangjun
2013-11-01
Full Text Available A new method of tracking the near space hypersonic vehicle is put forward. According to hypersonic vehicles’ characteristics, we improved current statistical model through online identification of the maneuvering frequency. A Monte Carlo simulation is used to analyze the performance of the method. The results show that the improved method exhibits very good tracking performance in comparison with the old method.
Hierarchical modelling for the environmental sciences statistical methods and applications
Clark, James S
2006-01-01
New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.
Octet magnetic Moments and their sum rules in statistical model
Batra, M
2013-01-01
The statistical model is implemented to find the magnetic moments of all octet baryons. The well-known sum rules like GMO and CG sum rules has been checked in order to check the consistency of our approach. The small discrepancy between the results suggests the importance of breaking in SU(3) symmetry.
Environmental Concern and Sociodemographic Variables: A Study of Statistical Models
Xiao, Chenyang; McCright, Aaron M.
2007-01-01
Studies of the social bases of environmental concern over the past 30 years have produced somewhat inconsistent results regarding the effects of sociodemographic variables, such as gender, income, and place of residence. The authors argue that model specification errors resulting from violation of two statistical assumptions (interval-level…
Statistical sampling and modelling for cork oak and eucalyptus stands
Paulo, M.J.
2002-01-01
This thesis focuses on the use of modern statistical methods to solve problems on sampling, optimal cutting time and agricultural modelling in Portuguese cork oak and eucalyptus stands. The results are contained in five chapters that have been submitted for publication as scientific manuscripts.The
Monte-Carlo simulation-based statistical modeling
Chen, John
2017-01-01
This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
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.
Statistical mechanics models for motion and force planning
Rodriguez, G.
1990-01-01
The models of statistical mechanics provide an alternative to the methods of classical mechanics more traditionally used in robotics. They have a potential to: improve analysis of object collisions; handle kinematic and dynamic contact interactions within the same frmework; and reduce the need for perfect deterministic world model information. The statistical mechanics models characterize the state of the system as a probability density function (p.d.f.) whose time evolution is governed by a partial differential equation subject to boundary and initial conditions. The boundary conditions when rigid objects collide reflect the conservation of momentum. The models are being developed to embedd in remote semi-autonomous systems with a need to reason and interact with a multiobject environment.
An Order Statistics Approach to the Halo Model for Galaxies
Paul, Niladri; Sheth, Ravi K
2016-01-01
We use the Halo Model to explore the implications of assuming that galaxy luminosities in groups are randomly drawn from an underlying luminosity function. We show that even the simplest of such order statistics models -- one in which this luminosity function $p(L)$ is universal -- naturally produces a number of features associated with previous analyses based on the `central plus Poisson satellites' hypothesis. These include the monotonic relation of mean central luminosity with halo mass, the Lognormal distribution around this mean, and the tight relation between the central and satellite mass scales. In stark contrast to observations of galaxy clustering, however, this model predicts $\\textit{no}$ luminosity dependence of large scale clustering. We then show that an extended version of this model, based on the order statistics of a $\\textit{halo mass dependent}$ luminosity function $p(L|m)$, is in much better agreement with the clustering data as well as satellite luminosities, but systematically under-pre...
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.......e. the heat dynamics of the building, have been developed. The models can be used to obtain rather detailed knowledge of the energy performance of the building and to optimize the control of the energy consumption for heating, which will be vital in conditions with increasing fluctuation of the energy supply...... 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...
Applying the luminosity function statistics in the fireshell model
Rangel Lemos, L. J.; Bianco, C. L.; Ruffini, R.
2015-12-01
The luminosity function (LF) statistics applied to the data of BATSE, GBM/Fermi and BAT/Swift is the theme approached in this work. The LF is a strong statistical tool to extract useful information from astrophysical samples, and the key point of this statistical analysis is in the detector sensitivity, where we have performed careful analysis. We applied the tool of the LF statistics to three GRB classes predicted by the Fireshell model. We produced, by LF statistics, predicted distributions of: peak ux N(Fph pk), redshift N(z) and peak luminosity N(Lpk) for the three GRB classes predicted by Fireshell model; we also used three GRB rates. We looked for differences among the distributions, and in fact we found. We performed a comparison between the distributions predicted and observed (with and without redshifts), where we had to build a list with 217 GRBs with known redshifts. Our goal is transform the GRBs in a standard candle, where a alternative is find a correlation between the isotropic luminosity and the Band peak spectral energy (Liso - Epk).
Statistical multiscale image segmentation via Alpha-stable modeling
Wan, Tao; Canagarajah, CN; Achim, AM
2007-01-01
This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by symmetric alpha-stable (SalphaS) distributions. These features are efficiently combined with the dominant color feature to perform automatic segmentation. First, the image is roughly segmented into textured and nontextured regions using the dual-tree complex wavelet transform (DT-CWT) with the sub-band coefficients modeled as SalphaS random variables. A mul-tiscale segmentation is ...
Generalized statistical model for multicomponent adsorption equilibria on zeolites
Energy Technology Data Exchange (ETDEWEB)
Rota, R.; Gamba, G.; Paludetto, R.; Carra, S.; Morbidelli, M. (Dipartimento di Chimica Fisica Applicata, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (IT))
1988-05-01
The statistical thermodynamic approach to multicomponent adsorption equilibria on zeolites has been extended to nonideal systems, through the correction of cross coefficients characterizing the interaction between unlike molecules. Estimation of the model parameters requires experimental binary equilibrium data. Comparisons with the classical model based on adsorbed solution theory are reported for three nonideal ternary systems. The two approaches provide comparable results in the simulation of binary and ternary adsorption equilibrium data at constant temperature and pressure.
Development of 3D statistical mandible models for cephalometric measurements
2012-01-01
Purpose 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. Materials and Methods 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 ...
Bregman divergence as general framework to estimate unnormalized statistical models
Gutmann, Michael
2012-01-01
We show that the Bregman divergence provides a rich framework to estimate unnormalized statistical models for continuous or discrete random variables, that is, models which do not integrate or sum to one, respectively. We prove that recent estimation methods such as noise-contrastive estimation, ratio matching, and score matching belong to the proposed framework, and explain their interconnection based on supervised learning. Further, we discuss the role of boosting in unsupervised learning.
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
Advances on statistical/thermodynamical models for unpolarized structure functions
Trevisan, Luis A.; Mirez, Carlos; Tomio, Lauro
2013-03-01
During the eights and nineties many statistical/thermodynamical models were proposed to describe the nucleons' structure functions and distribution of the quarks in the hadrons. Most of these models describe the compound quarks and gluons inside the nucleon as a Fermi / Bose gas respectively, confined in a MIT bag[1] with continuous energy levels. Another models considers discrete spectrum. Some interesting features of the nucleons are obtained by these models, like the sea asymmetries ¯d/¯u and ¯d-¯u.
STATISTICAL MODELS FOR SEMI-RIGID NEMATIC POLYMERS
Institute of Scientific and Technical Information of China (English)
WANG Xinjiu
1995-01-01
Semi-rigid liquid crystal polymer is a class of liquid crystal polymers different from long rigid rod liquid crystal polymer to which the well-known Onsager and Flory theories are applied. In this paper, three statistical models for the semi-rigid nematic polymer were addressed. They are the elastically jointed rod model, worm-like chain model, and non-homogeneous chain model.The nematic-isotropic transition temperature was examined. The pseudo-second transition temperature is expressed analytically. Comparisons with the experiments were made and the agreements were found.
The estimation of yearly probability gain for seismic statistical model
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Based on the calculation method of information gain in the stochastic process presented by Vere-Jones, the relation between information gain and probability gain is studied, which is very common in earthquake prediction, and the yearly probability gain for seismic statistical model is proposed. The method is applied to the non-stationary Poisson model with whole-process exponential increase and stress release model. In addition, the prediction method of stress release model is obtained based on the inverse function simulation method of stochastic variable.
Triage in medical practices: an unacceptable model?
Bell, Nora K
1981-12-01
Two basic presuppositions of triage are challenged: the utilitarian notion that it is morally preferable to save the greater number, and the extension of the salvageability concept as a model for macroallocation of scarce medical resources. The 'more is better' principle lacks the status of a universal moral obligation and leads to an impersonal view of the valuing of life. In the allocation of scarce resources, triage is suspect since life-saving decisions involve medical, social, economic, and political criteria. Triage thus perpetuates existing injustices.
Investigation of the freely available easy-to-use software 'EZR' for medical statistics.
Kanda, Y
2013-03-01
Although there are many commercially available statistical software packages, only a few implement a competing risk analysis or a proportional hazards regression model with time-dependent covariates, which are necessary in studies on hematopoietic SCT. In addition, most packages are not clinician friendly, as they require that commands be written based on statistical languages. This report describes the statistical software 'EZR' (Easy R), which is based on R and R commander. EZR enables the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates, receiver operating characteristics analyses, meta-analyses, sample size calculation and so on, by point-and-click access. EZR is freely available on our website (http://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html) and runs on both Windows (Microsoft Corporation, USA) and Mac OS X (Apple, USA). This report provides instructions for the installation and operation of EZR.
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.
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.
Level statistics of a pseudo-Hermitian Dicke model.
Deguchi, Tetsuo; Ghosh, Pijush K; Kudo, Kazue
2009-08-01
A non-Hermitian operator that is related to its adjoint through a similarity transformation is defined as a pseudo-Hermitian operator. We study the level statistics of a pseudo-Hermitian Dicke Hamiltonian that undergoes quantum phase transition (QPT). We find that the level-spacing distribution of this Hamiltonian near the integrable limit is close to Poisson distribution, while it is Wigner distribution for the ranges of the parameters for which the Hamiltonian is nonintegrable. We show that the assertion in the context of the standard Dicke model that QPT is a precursor to a change in the level statistics is not valid in general.
Convex Combination of Multiple Statistical Models with Application to VAD
DEFF Research Database (Denmark)
Petsatodis, Theodoros; Boukis, Christos; Talantzis, Fotios
2011-01-01
This paper proposes a robust Voice Activity Detector (VAD) based on the observation that the distribution of speech captured with far-field microphones is highly varying, depending on the noise and reverberation conditions. The proposed VAD employs a convex combination scheme comprising three...... statistical distributions - a Gaussian, a Laplacian, and a two-sided Gamma - to effectively model captured speech. This scheme shows increased ability to adapt to dynamic acoustic environments. The contribution of each distribution to this convex combination is automatically adjusted based on the statistical...
General Linear Models: An Integrated Approach to Statistics
Directory of Open Access Journals (Sweden)
Andrew Faulkner
2008-09-01
Full Text Available Generally, in psychology, the various statistical analyses are taught independently from each other. As a consequence, students struggle to learn new statistical analyses, in contexts that differ from their textbooks. This paper gives a short introduction to the general linear model (GLM, in which it is showed that ANOVA (one-way, factorial, repeated measure and analysis of covariance is simply a multiple correlation/regression analysis (MCRA. Generalizations to other cases, such as multivariate and nonlinear analysis, are also discussed. It can easily be shown that every popular linear analysis can be derived from understanding MCRA.
Statistical skull models from 3D X-ray images
Berar, M; Bailly, G; Payan, Y; Berar, Maxime; Desvignes, Michel; Payan, Yohan
2006-01-01
We present 2 statistical models of the skull and mandible built upon an elastic registration method of 3D meshes. The aim of this work is to relate degrees of freedom of skull anatomy, as static relations are of main interest for anthropology and legal medicine. Statistical models can effectively provide reconstructions together with statistical precision. In our applications, patient-specific meshes of the skull and the mandible are high-density meshes, extracted from 3D CT scans. All our patient-specific meshes are registrated in a subject-shared reference system using our 3D-to-3D elastic matching algorithm. Registration is based upon the minimization of a distance between the high density mesh and a shared low density mesh, defined on the vertexes, in a multi resolution approach. A Principal Component analysis is performed on the normalised registrated data to build a statistical linear model of the skull and mandible shape variation. The accuracy of the reconstruction is under the millimetre in the shape...
On Wiener filtering and the physics behind statistical modeling.
Marbach, Ralf
2002-01-01
The closed-form solution of the so-called statistical multivariate calibration model is given in terms of the pure component spectral signal, the spectral noise, and the signal and noise of the reference method. The "statistical" calibration model is shown to be as much grounded on the physics of the pure component spectra as any of the "physical" models. There are no fundamental differences between the two approaches since both are merely different attempts to realize the same basic idea, viz., the spectrometric Wiener filter. The concept of the application-specific signal-to-noise ratio (SNR) is introduced, which is a combination of the two SNRs from the reference and the spectral data. Both are defined and the central importance of the latter for the assessment and development of spectroscopic instruments and methods is explained. Other statistics like the correlation coefficient, prediction error, slope deficiency, etc., are functions of the SNR. Spurious correlations and other practically important issues are discussed in quantitative terms. Most important, it is shown how to use a priori information about the pure component spectra and the spectral noise in an optimal way, thereby making the distinction between statistical and physical calibrations obsolete and combining the best of both worlds. Companies and research groups can use this article to realize significant savings in cost and time for development efforts.
Deriving statistical significance maps for support vector regression using medical imaging data.
Gaonkar, Bilwaj; Sotiras, Aristeidis; Davatzikos, Christos
2013-01-01
Regression analysis involves predicting a continuous variable using imaging data. The Support Vector Regression (SVR) algorithm has previously been used in addressing regression analysis in neuroimaging. However, identifying the regions of the image that the SVR uses to model the dependence of a target variable remains an open problem. It is an important issue when one wants to biologically interpret the meaning of a pattern that predicts the variable(s) of interest, and therefore to understand normal or pathological process. One possible approach to the identification of these regions is the use of permutation testing. Permutation testing involves 1) generation of a large set of 'null SVR models' using randomly permuted sets of target variables, and 2) comparison of the SVR model trained using the original labels to the set of null models. These permutation tests often require prohibitively long computational time. Recent work in support vector classification shows that it is possible to analytically approximate the results of permutation testing in medical image analysis. We propose an analogous approach to approximate permutation testing based analysis for support vector regression with medical imaging data. In this paper we present 1) the theory behind our approximation, and 2) experimental results using two real datasets.
USING ARTIFICIAL NEURAL NETWORKS AS STATISTICAL TOOLS FOR ANALYSIS OF MEDICAL DATA
Directory of Open Access Journals (Sweden)
ANOUSHIRAVAN KAZEMNEZHAD
2003-06-01
Full Text Available Introduction: Artificial neural networks mimic brains behavior. They are able to predict and feature recognition and classification. Therefore, neural networks seem to serious rivals for statistical models like regression and discriminant analysis. Methods: We have introduced biological neuron and generalized their function for artificial neurons and described back propagation error algoritm for training of networks in details. Result: Based on two simulated data and one real data we built neural networks by using back propagation and compared them by regression models. Discussion: Neural networks can be considered as a non parametric method for data modeling and seem that they are potentially. more powerful than regression for modeling, but more ambiguous in notation.
Statistical traffic modeling of MPEG frame size: Experiments and Analysis
Directory of Open Access Journals (Sweden)
Haniph A. Latchman
2009-12-01
Full Text Available For guaranteed quality of service (QoS and sufficient bandwidth in a communication network which provides an integrated multimedia service, it is important to obtain an analytical and tractable model of the compressed MPEG data. This paper presents a statistical approach to a group of picture (GOP MPEG frame size model to increase network traffic performance in a communication network. We extract MPEG frame data from commercial DVD movies and make probability histograms to analyze the statistical characteristics of MPEG frame data. Six candidates of probability distributions are considered here and their parameters are obtained from the empirical data using the maximum likelihood estimation (MLE. This paper shows that the lognormal distribution is the best fitting model of MPEG-2 total frame data.
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.
Statistical 3D damage accumulation model for ion implant simulators
Energy Technology Data Exchange (ETDEWEB)
Hernandez-Mangas, J.M. E-mail: jesman@ele.uva.es; Lazaro, J.; Enriquez, L.; Bailon, L.; Barbolla, J.; Jaraiz, M
2003-04-01
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.
Experimental, statistical, and biological models of radon carcinogenesis
Energy Technology Data Exchange (ETDEWEB)
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.
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.
A statistical model for characterization of histopathology images
Álvarez, Pablo; Castro, Guatizalema; Corredor, Germán.; Romero, Eduardo
2015-01-01
Accessing information of interest in collections of histopathology images is a challenging task. To address such issue, previous works have designed searching strategies based on the use of keywords and low-level features. However, those methods have demonstrated to not be enough or practical for this purpose. Alternative low-level features such as cell area, distance among cells and cell density are directly associated to simple histological concepts and could serve as good descriptors for this purpose. In this paper, a statistical model is adapted to represent the distribution of the areas occupied by cells for its use in whole histopathology image characterization. This novel descriptor facilitates the design of metrics based on distribution parameters and also provides new elements for a better image understanding. The proposed model was validated using image processing and statistical techniques. Results showed low error rates, demonstrating the accuracy of the model.
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.
Improved head-driven statistical models for natural language parsing
Institute of Scientific and Technical Information of China (English)
袁里驰
2013-01-01
Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.
Statistical procedures for evaluating daily and monthly hydrologic model predictions
Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.
2004-01-01
The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.
Editorial to: Six papers on Dynamic Statistical Models
DEFF Research Database (Denmark)
2014-01-01
The following six papers are based on invited lectures at the satellite meeting held at the University of Copenhagen before the 58th World Statistics Congress of the International Statistical Institute in Dublin in 2011. At the invitation of the Bernoulli Society, the satellite meeting was organi......The following six papers are based on invited lectures at the satellite meeting held at the University of Copenhagen before the 58th World Statistics Congress of the International Statistical Institute in Dublin in 2011. At the invitation of the Bernoulli Society, the satellite meeting...... areas working with frontier research topics in statistics for dynamic models. This issue of SJS contains a quite diverse collection of six papers from the conference: Spectral Estimation of Covolatility from Noisy Observations Using Local Weights Markus Bibinger and Markus Reiß One-Way Anova...... of Copenhagen Program of Excellence and Elsevier. We would also like to thank the authors for contributing interesting papers, the referees for their helpful reports, and the present and previous editors of SJS for their support of the publication of the papers from the satellite meeting....
Physics-based statistical learning approach to mesoscopic model selection
Taverniers, Søren; Haut, Terry S.; Barros, Kipton; Alexander, Francis J.; Lookman, Turab
2015-11-01
In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.
[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.
The Integrated Medical Model: Outcomes from Independent Review
Myers, J.; Garcia, Y.; Griffin, D.; Arellano, J.; Boley, L.; Goodenow, D. A.; Kerstman, E.; Reyes, D.; Saile, L.; Walton, M.;
2017-01-01
In 2016, the Integrated Medical Model (IMM) v4.0 underwent an extensive external review in preparation for transition to an operational status. In order to insure impartiality of the review process, the Exploration Medical Capabilities Element of NASA's Human Research Program convened the review through the Systems Review Office at NASA Goddard Space Flight Center (GSFC). The review board convened by GSFC consisted of persons from both NASA and academia with expertise in the fields of statistics, epidemiology, modeling, software development, aerospace medicine, and project management (see Figure 1). The board reviewed software and code standards, as well as evidence pedigree associated with both the input and outcomes information. The board also assesses the models verification, validation, sensitivity to parameters and ability to answer operational questions. This talk will discuss the processes for designing the review, how the review progressed and the findings from the board, as well as summarize the IMM project responses to those findings. Overall, the board found that the IMM is scientifically sound, represents a necessary, comprehensive approach to identifying medical and environmental risks facing astronauts in long duration missions and is an excellent tool for communication between engineers and physicians. The board also found IMM and its customer(s) should convene an additional review of the IMM data sources and to develop a sustainable approach to augment, peer review, and maintain the information utilized in the IMM. The board found this is critically important because medical knowledge continues to evolve. Delivery of IMM v4.0 to the Crew Health and Safety (CHS) Program will occur in the 2017. Once delivered for operational decision support, IMM v4.0 will provide CHS with additional quantitative capability in to assess astronaut medical risks and required medical capabilities to help drive down overall mission risks.
Nuclear EMC effect in non-extensive statistical model
Trevisan, Luis A.; Mirez, Carlos
2013-05-01
In the present work, we attempt to describe the nuclear EMC effect by using the proton structure functions obtained from the non-extensive statistical quark model. We record that such model has three fundamental variables, the temperature T, the radius, and the Tsallis parameter q. By combining different small changes, a good agreement with the experimental data may be obtained. Another interesting point of the model is to allow phenomenological interpretation, for instance, with q constant and changing the radius and the temperature or changing the radius and q and keeping the temperature.
New statistical lattice model with double honeycomb symmetry
Naji, S.; Belhaj, A.; Labrim, H.; Bhihi, M.; Benyoussef, A.; El Kenz, A.
2014-04-01
Inspired from the connection between Lie symmetries and two-dimensional materials, we propose a new statistical lattice model based on a double hexagonal structure appearing in the G2 symmetry. We first construct an Ising-1/2 model, with spin values σ = ±1, exhibiting such a symmetry. The corresponding ground state shows the ferromagnetic, the antiferromagnetic, the partial ferrimagnetic and the topological ferrimagnetic phases depending on the exchange couplings. Then, we examine the phase diagrams and the magnetization using the mean field approximation (MFA). Among others, it has been suggested that the present model could be localized between systems involving the triangular and the single hexagonal lattice geometries.
Statistical shape model with random walks for inner ear segmentation
DEFF Research Database (Denmark)
Pujadas, Esmeralda Ruiz; Kjer, Hans Martin; Piella, Gemma
2016-01-01
Cochlear implants can restore hearing to completely or partially deaf patients. The intervention planning can be aided by providing a patient-specific model of the inner ear. Such a model has to be built from high resolution images with accurate segmentations. Thus, a precise segmentation...... is required. We propose a new framework for segmentation of micro-CT cochlear images using random walks combined with a statistical shape model (SSM). The SSM allows us to constrain the less contrasted areas and ensures valid inner ear shape outputs. Additionally, a topology preservation method is proposed...
Think continuous: Markovian Gaussian models in spatial statistics
Simpson, Daniel; Rue, Håvard
2011-01-01
Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spatial statistics. Unfortunately, it has traditionally been difficult to link GMRFs with the more traditional Gaussian random field models as the Markov property is difficult to deploy in continuous space. Following the pioneering work of Lindgren et al. (2011), we expound on the link between Markovian Gaussian random fields and GMRFs. In particular, we discuss the theoretical and practical aspects of fast computation with continuously specified Markovian Gaussian random fields, as well as the clear advantages they offer in terms of clear, parsimonious and interpretable models of anisotropy and non-stationarity.
Spatio-temporal statistical models with applications to atmospheric processes
Energy Technology Data Exchange (ETDEWEB)
Wikle, C.K.
1996-12-31
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.
Spatio-temporal statistical models with applications to atmospheric processes
Energy Technology Data Exchange (ETDEWEB)
Wikle, Christopher K. [Iowa State Univ., Ames, IA (United States)
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.
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.
RANDOM SYSTEMS OF HARD PARTICLES:MODELS AND STATISTICS
Institute of Scientific and Technical Information of China (English)
Dietrich Stoyan
2002-01-01
This paper surveys models and statistical properties of random systems of hard particles. Such systems appear frequently in materials science, biology and elsewhere. In mathematical - statistical investigations, simulations of such structures play an important role. In these simulations various methods and models are applied, namely the RSA model, sedimentation and collective rearrangement algorithms, molecular dynamics, and Monte Carlo methods such as the Metropolis - Hastings algorithm. The statistical description of real and simulated particle systems uses ideas of the mathematical theories of random sets and point processes. This leads to characteristics such as volume fraction or porosity, covariance,contact distribution functions, specific connectivity number from the random set approach and intensity, pair correlation function and mark correlation functions from the point process approach. Some of them can be determined stereologically using planar sections, while others can only be obtained using three - dimensional data and 3D image analysis. They are valuable tools for fitting models to empirical data and, consequently, for understanding various materials, biological structures, porous media and other practically important spatial structures.
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.
Statistical model of clutter suppression in tissue harmonic imaging
Yan, Xiang; Hamilton, Mark F.
2011-01-01
A statistical model is developed for the suppression of clutter in tissue harmonic imaging (THI). Tissue heterogeneity is modeled as a random phase screen that is characterized by its correlation length and variance. With the autocorrelation function taken to be Gaussian and for small variance, statistical solutions are derived for the mean intensities at the fundamental and second-harmonic frequencies in the field of a focused sound beam that propagates through the phase screen. The statistical solutions are verified by comparison with ensemble averaging of direct numerical simulations. The model demonstrates that THI reduces the aberration clutter appearing in the focal region regardless of the depth of the aberrating layer, with suppression of the clutter most effective when the layer is close to the source. The model is also applied to the reverberation clutter that is transmitted forward along the axis of the beam. As with aberration clutter, suppression of such reverberation clutter by THI is most pronounced when the tissue heterogeneity is located close to the source. PMID:21428483
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.
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.
Real-Time Statistical Modeling of Blood Sugar.
Otoom, Mwaffaq; Alshraideh, Hussam; Almasaeid, Hisham M; López-de-Ipiña, Diego; Bravo, José
2015-10-01
Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient's historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.
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
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...
Statistical mechanics models for multimode lasers and random lasers
Antenucci, F; Berganza, M Ibáñez; Marruzzo, A; Leuzzi, L
2015-01-01
We review recent statistical mechanical approaches to multimode laser theory. The theory has proved very effective to describe standard lasers. We refer of the mean field theory for passive mode locking and developments based on Monte Carlo simulations and cavity method to study the role of the frequency matching condition. The status for a complete theory of multimode lasing in open and disordered cavities is discussed and the derivation of the general statistical models in this framework is presented. When light is propagating in a disordered medium, the system can be analyzed via the replica method. For high degrees of disorder and nonlinearity, a glassy behavior is expected at the lasing threshold, providing a suggestive link between glasses and photonics. We describe in details the results for the general Hamiltonian model in mean field approximation and mention an available test for replica symmetry breaking from intensity spectra measurements. Finally, we summary some perspectives still opened for such...
Passive Target Tracking Based on Current Statistical Model
Institute of Scientific and Technical Information of China (English)
DENG Xiao-long; XIE Jian-ying; YANG Yu-pu
2005-01-01
Bearing-only passive tracking is regarded as a nonlinear hard tracking problem. There are still no completely good solutions to this problem until now. Based on current statistical model, the novel solution to this problem utilizing particle filter (PF) and the unscented Kalman filter (UKF) is proposed. The new solution adopts data fusion from two observers to increase the observability of passive tracking. It applies the residual resampling step to reduce the degeneracy of PF and it introduces the Markov Chain Monte Carlo methods (MCMC) to reduce the effect of the "sample impoverish". Based on current statistical model, the EKF, the UKF and particle filter with various proposal distributions are compared in the passive tracking experiments with two observers. The simulation results demonstrate the good performance of the proposed new filtering methods with the novel techniques.
Statistical detection of structural damage based on model reduction
Institute of Scientific and Technical Information of China (English)
Tao YIN; Heung-fai LAM; Hong-ping ZHU
2009-01-01
This paper proposes a statistical method for damage detection based on the finite element (FE) model reduction technique that utilizes measured modal data with a limited number of sensors.A deterministic damage detection process is formulated based on the model reduction technique.The probabilistic process is integrated into the deterministic damage detection process using a perturbation technique,resulting in a statistical structural damage detection method.This is achieved by deriving the firstand second-order partial derivatives of uncertain parameters,such as elasticity of the damaged member,with respect to the measurement noise,which allows expectation and covariance matrix of the uncertain parameters to be calculated.Besides the theoretical development,this paper reports numerical verification of the proposed method using a portal frame example and Monte Carlo simulation.
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.
Exploring Explanations of Subglacial Bedform Sizes Using Statistical Models.
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John K Hillier
Full Text Available Sediments beneath modern ice sheets exert a key control on their flow, but are largely inaccessible except through geophysics or boreholes. In contrast, palaeo-ice sheet beds are accessible, and typically characterised by numerous bedforms. However, the interaction between bedforms and ice flow is poorly constrained and it is not clear how bedform sizes might reflect ice flow conditions. To better understand this link we present a first exploration of a variety of statistical models to explain the size distribution of some common subglacial bedforms (i.e., drumlins, ribbed moraine, MSGL. By considering a range of models, constructed to reflect key aspects of the physical processes, it is possible to infer that the size distributions are most effectively explained when the dynamics of ice-water-sediment interaction associated with bedform growth is fundamentally random. A 'stochastic instability' (SI model, which integrates random bedform growth and shrinking through time with exponential growth, is preferred and is consistent with other observations of palaeo-bedforms and geophysical surveys of active ice sheets. Furthermore, we give a proof-of-concept demonstration that our statistical approach can bridge the gap between geomorphological observations and physical models, directly linking measurable size-frequency parameters to properties of ice sheet flow (e.g., ice velocity. Moreover, statistically developing existing models as proposed allows quantitative predictions to be made about sizes, making the models testable; a first illustration of this is given for a hypothesised repeat geophysical survey of bedforms under active ice. Thus, we further demonstrate the potential of size-frequency distributions of subglacial bedforms to assist the elucidation of subglacial processes and better constrain ice sheet models.
Wiles, Andrew D; Likholyot, Alexander; Frantz, Donald D; Peters, Terry M
2008-03-01
Error models associated with point-based medical image registration problems were first introduced in the late 1990s. The concepts of fiducial localizer error, fiducial registration error, and target registration error are commonly used in the literature. The model for estimating the target registration error at a position r in a coordinate frame defined by a set of fiducial markers rigidly fixed relative to one another is ubiquitous in the medical imaging literature. The model has also been extended to simulate the target registration error at the point of interest in optically tracked tools. However, the model is limited to describing the error in situations where the fiducial localizer error is assumed to have an isotropic normal distribution in R3. In this work, the model is generalized to include a fiducial localizer error that has an anisotropic normal distribution. Similar to the previous models, the root mean square statistic rms tre is provided along with an extension that provides the covariance Sigma tre. The new model is verified using a Monte Carlo simulation and a set of statistical hypothesis tests. Finally, the differences between the two assumptions, isotropic and anisotropic, are discussed within the context of their use in 1) optical tool tracking simulation and 2) image registration.
Statistical Quark Model for the Nucleon Structure Function
Mirez, Carlos; Tomio, Lauro; Trevisan, Luis A.; Frederico, Tobias
2009-06-01
A statistical quark model, with quark energy levels given by a central linear confining potential is used to obtain the light sea-quark asymmetry, d¯/ū, and also for the ratio d/u, inside the nucleon. After adjusting a temperature parameter by the Gottfried sum rule violation, and chemical potentials by the valence up and down quark normalizations, the results are compared with experimental data available.
A statistical mechanics model of carbon nanotube macro-films
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
Carbon nanotube macro-films are two-dimensional films with micrometer thickness and centimeter by centimeter in-plane dimension.These carbon nanotube macroscopic assemblies have attracted significant attention from the material and mechanics communities recently because they can be easily handled and tailored to meet specific engineering needs.This paper reports the experimental methods on the preparation and characterization of single-walled carbon nanotube macro-films,and a statistical mechanics model on ...
Physical-Statistical Model of Thermal Conductivity of Nanofluids
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B. Usowicz
2014-01-01
Full Text Available A physical-statistical model for predicting the effective thermal conductivity of nanofluids is proposed. The volumetric unit of nanofluids in the model consists of solid, liquid, and gas particles and is treated as a system made up of regular geometric figures, spheres, filling the volumetric unit by layers. The model assumes that connections between layers of the spheres and between neighbouring spheres in the layer are represented by serial and parallel connections of thermal resistors, respectively. This model is expressed in terms of thermal resistance of nanoparticles and fluids and the multinomial distribution of particles in the nanofluids. The results for predicted and measured effective thermal conductivity of several nanofluids (Al2O3/ethylene glycol-based and Al2O3/water-based; CuO/ethylene glycol-based and CuO/water-based; and TiO2/ethylene glycol-based are presented. The physical-statistical model shows a reasonably good agreement with the experimental results and gives more accurate predictions for the effective thermal conductivity of nanofluids compared to existing classical models.
The Ising model in physics and statistical genetics.
Majewski, J; Li, H; Ott, J
2001-10-01
Interdisciplinary communication is becoming a crucial component of the present scientific environment. Theoretical models developed in diverse disciplines often may be successfully employed in solving seemingly unrelated problems that can be reduced to similar mathematical formulation. The Ising model has been proposed in statistical physics as a simplified model for analysis of magnetic interactions and structures of ferromagnetic substances. Here, we present an application of the one-dimensional, linear Ising model to affected-sib-pair (ASP) analysis in genetics. By analyzing simulated genetics data, we show that the simplified Ising model with only nearest-neighbor interactions between genetic markers has statistical properties comparable to much more complex algorithms from genetics analysis, such as those implemented in the Allegro and Mapmaker-Sibs programs. We also adapt the model to include epistatic interactions and to demonstrate its usefulness in detecting modifier loci with weak individual genetic contributions. A reanalysis of data on type 1 diabetes detects several susceptibility loci not previously found by other methods of analysis.
Statistical mechanics of the Huxley-Simmons model.
Caruel, M; Truskinovsky, L
2016-06-01
The chemomechanical model of Huxley and Simmons (HS) [A. F. Huxley and R. M. Simmons, Nature 233, 533 (1971)NATUAS0028-083610.1038/233533a0] provides a paradigmatic description of mechanically induced collective conformational changes relevant in a variety of biological contexts, from muscles power stroke and hair cell gating to integrin binding and hairpin unzipping. We develop a statistical mechanical perspective on the HS model by exploiting a formal analogy with a paramagnetic Ising model. We first study the equilibrium HS model with a finite number of elements and compute explicitly its mechanical and thermal properties. To model kinetics, we derive a master equation and solve it for several loading protocols. The developed formalism is applicable to a broad range of allosteric systems with mean-field interactions.
Statistical mechanics of the Huxley-Simmons model
Caruel, M
2016-01-01
The chemomechanical model of Huxley and Simmons (HS) [A. F. Huxley and R. M. Simmons, Nature 233, 533 (1971)] provides a paradigmatic description of mechanically induced collective conformational changes relevant in a variety of biological contexts, from muscles power-stroke and hair cell gating to integrin binding and hairpin unzipping. We develop a statistical mechanical perspective on the HS model by exploiting a formal analogy with a paramagnetic Ising model. We first study the equilibrium HS model with a finite number of elements and compute explicitly its mechanical and thermal properties. To model kinetics, we derive a master equation and solve it for several loading protocols. The developed formalism is applicable to a broad range of allosteric systems with mean-field interactions.
Statistical mechanics of the Huxley-Simmons model
Caruel, M.; Truskinovsky, L.
2016-06-01
The chemomechanical model of Huxley and Simmons (HS) [A. F. Huxley and R. M. Simmons, Nature 233, 533 (1971), 10.1038/233533a0] provides a paradigmatic description of mechanically induced collective conformational changes relevant in a variety of biological contexts, from muscles power stroke and hair cell gating to integrin binding and hairpin unzipping. We develop a statistical mechanical perspective on the HS model by exploiting a formal analogy with a paramagnetic Ising model. We first study the equilibrium HS model with a finite number of elements and compute explicitly its mechanical and thermal properties. To model kinetics, we derive a master equation and solve it for several loading protocols. The developed formalism is applicable to a broad range of allosteric systems with mean-field interactions.
A generalized statistical model for the size distribution of wealth
Clementi, F.; Gallegati, M.; Kaniadakis, G.
2012-12-01
In a recent paper in this journal (Clementi et al 2009 J. Stat. Mech. P02037), we proposed a new, physically motivated, distribution function for modeling individual incomes, having its roots in the framework of the κ-generalized statistical mechanics. The performance of the κ-generalized distribution was checked against real data on personal income for the United States in 2003. In this paper we extend our previous model so as to be able to account for the distribution of wealth. Probabilistic functions and inequality measures of this generalized model for wealth distribution are obtained in closed form. In order to check the validity of the proposed model, we analyze the US household wealth distributions from 1984 to 2009 and conclude an excellent agreement with the data that is superior to any other model already known in the literature.
A Statistical Model for In Vivo Neuronal Dynamics.
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Simone Carlo Surace
Full Text Available Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. We finally show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.
Spatial Statistical Procedures to Validate Input Data in Energy Models
Energy Technology Data Exchange (ETDEWEB)
Johannesson, G.; Stewart, J.; Barr, C.; Brady Sabeff, L.; George, R.; Heimiller, D.; Milbrandt, A.
2006-01-01
Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the abovementioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory and modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.
Spatial Statistical Procedures to Validate Input Data in Energy Models
Energy Technology Data Exchange (ETDEWEB)
Lawrence Livermore National Laboratory
2006-01-27
Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy-related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the above-mentioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory and modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.
Modelling degradation of bioresorbable polymeric medical devices
Pan, J
2015-01-01
The use of bioresorbable polymers in stents, fixation devices and tissue engineering is revolutionising medicine. Both industry and academic researchers are interested in using computer modelling to replace some experiments which are costly and time consuming. This book provides readers with a comprehensive review of modelling polymers and polymeric medical devices as an alternative to practical experiments. Chapters in part one provide readers with an overview of the fundamentals of biodegradation. Part two looks at a wide range of degradation theories for bioresorbable polymers and devices.
Calculation of statistical entropic measures in a model of solids
Sanudo, Jaime
2012-01-01
In this work, a one-dimensional model of crystalline solids based on the Dirac comb limit of the Kronig-Penney model is considered. From the wave functions of the valence electrons, we calculate a statistical measure of complexity and the Fisher-Shannon information for the lower energy electronic bands appearing in the system. All these magnitudes present an extremal value for the case of solids having half-filled bands, a configuration where in general a high conductivity is attained in real solids, such as it happens with the monovalent metals.
Linguistically motivated statistical machine translation models and algorithms
Xiong, Deyi
2015-01-01
This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.
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.
Efficient Parallel Statistical Model Checking of Biochemical Networks
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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.
Non-gaussianity and Statistical Anisotropy in Cosmological Inflationary Models
Valenzuela-Toledo, Cesar A
2010-01-01
We study the statistical descriptors for some cosmological inflationary models that allow us to get large levels of non-gaussianity and violations of statistical isotropy. Basically, we study two different class of models: a model that include only scalar field perturbations, specifically a subclass of small-field slow-roll models of inflation with canonical kinetic terms, and models that admit both vector and scalar field perturbations. We study the former to show that it is possible to attain very high, including observable, values for the levels of non-gaussianity f_{NL} and \\tao_{NL} in the bispectrum B_\\zeta and trispectrum T_\\zeta of the primordial curvature perturbation \\zeta respectively. Such a result is obtained by taking care of loop corrections in the spectrum P_\\zeta, the bispectrum B_\\zeta and the trispectrum T_\\zeta . Sizeable values for f_{NL} and \\tao_{NL} arise even if \\zeta is generated during inflation. For the latter we study the spectrum P_\\zeta, bispectrum B_\\zeta and trispectrum $T_\\ze...
Anyonic behavior of an intermediate-statistics fermion gas model.
Algin, Abdullah; Irk, Dursun; Topcu, Gozde
2015-06-01
We study the high-temperature behavior of an intermediate-statistics fermionic gas model whose quantum statistical properties enable us to effectively deduce the details about both the interaction among deformed (quasi)particles and their anyonic behavior. Starting with a deformed fermionic grand partition function, we calculate, in the thermodynamical limit, several thermostatistical functions of the model such as the internal energy and the entropy by means of a formalism of the fermionic q calculus. For high temperatures, a virial expansion of the equation of state for the system is obtained in two and three dimensions and the first five virial coefficients are derived in terms of the model deformation parameter q. From the results obtained by the effect of fermionic deformation, it is found that the model parameter q interpolates completely between bosonlike and fermionic systems via the behaviors of the third and fifth virial coefficients in both two and three spatial dimensions and in addition it characterizes effectively the interaction among quasifermions. Our results reveal that the present deformed (quasi)fermion model could be very efficient and effective in accounting for the nonlinear behaviors in interacting composite particle systems.
A statistical permafrost distribution model for the European Alps
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L. Boeckli
2011-05-01
Full Text Available Permafrost distribution modeling in densely populated mountain regions is an important task to support the construction of infrastructure and for the assessment of climate change effects on permafrost and related natural systems. In order to analyze permafrost distribution and evolution on an Alpine-wide scale, one consistent model for the entire domain is needed.
We present a statistical permafrost model for the entire Alps based on rock glacier inventories and rock surface temperatures. Starting from an integrated model framework, two different sub-models were developed, one for debris covered areas (debris model and one for steep rock faces (rock model. For the debris model a generalized linear mixed-effect model (GLMM was used to predict the probability of a rock glacier being intact as opposed to relict. The model is based on the explanatory variables mean annual air temperature (MAAT, potential incoming solar radiation (PISR and the mean annual sum of precipitation (PRECIP, and achieves an excellent discrimination (area under the receiver-operating characteristic, AUROC = 0.91. Surprisingly, the probability of a rock glacier being intact is positively associated with increasing PRECIP for given MAAT and PISR conditions. The rock model was calibrated with mean annual rock surface temperatures (MARST and is based on MAAT and PISR. The linear regression achieves a root mean square error (RMSE of 1.6 °C. The final model combines the two sub-models and accounts for the different scales used for model calibration. Further steps to transfer this model into a map-based product are outlined.
The Impact of Statistical Leakage Models on Design Yield Estimation
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Rouwaida Kanj
2011-01-01
Full Text Available Device mismatch and process variation models play a key role in determining the functionality and yield of sub-100 nm design. Average characteristics are often of interest, such as the average leakage current or the average read delay. However, detecting rare functional fails is critical for memory design and designers often seek techniques that enable accurately modeling such events. Extremely leaky devices can inflict functionality fails. The plurality of leaky devices on a bitline increase the dimensionality of the yield estimation problem. Simplified models are possible by adopting approximations to the underlying sum of lognormals. The implications of such approximations on tail probabilities may in turn bias the yield estimate. We review different closed form approximations and compare against the CDF matching method, which is shown to be most effective method for accurate statistical leakage modeling.
Statistical models of video structure for content analysis and characterization.
Vasconcelos, N; Lippman, A
2000-01-01
Content structure plays an important role in the understanding of video. In this paper, we argue that knowledge about structure can be used both as a means to improve the performance of content analysis and to extract features that convey semantic information about the content. We introduce statistical models for two important components of this structure, shot duration and activity, and demonstrate the usefulness of these models with two practical applications. First, we develop a Bayesian formulation for the shot segmentation problem that is shown to extend the standard thresholding model in an adaptive and intuitive way, leading to improved segmentation accuracy. Second, by applying the transformation into the shot duration/activity feature space to a database of movie clips, we also illustrate how the Bayesian model captures semantic properties of the content. We suggest ways in which these properties can be used as a basis for intuitive content-based access to movie libraries.
Liver recognition based on statistical shape model in CT images
Xiang, Dehui; Jiang, Xueqing; Shi, Fei; Zhu, Weifang; Chen, Xinjian
2016-03-01
In this paper, an automatic method is proposed to recognize the liver on clinical 3D CT images. The proposed method effectively use statistical shape model of the liver. Our approach consist of three main parts: (1) model training, in which shape variability is detected using principal component analysis from the manual annotation; (2) model localization, in which a fast Euclidean distance transformation based method is able to localize the liver in CT images; (3) liver recognition, the initial mesh is locally and iteratively adapted to the liver boundary, which is constrained with the trained shape model. We validate our algorithm on a dataset which consists of 20 3D CT images obtained from different patients. The average ARVD was 8.99%, the average ASSD was 2.69mm, the average RMSD was 4.92mm, the average MSD was 28.841mm, and the average MSD was 13.31%.
Estimating Predictive Variance for Statistical Gas Distribution Modelling
Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo
2009-05-01
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.
Statistical Inference for Partially Linear Regression Models with Measurement Errors
Institute of Scientific and Technical Information of China (English)
Jinhong YOU; Qinfeng XU; Bin ZHOU
2008-01-01
In this paper, the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors. Firstly,a bandwidth selection procedure is proposed, which is a combination of the difference-based technique and GCV method. Secondly, a goodness-of-fit test procedure is proposed,which is an extension of the generalized likelihood technique. Thirdly, a variable selection procedure for the parametric part is provided based on the nonconcave penalization and corrected profile least squares. Same as "Variable selection via nonconcave penalized like-lihood and its oracle properties" (J. Amer. Statist. Assoc., 96, 2001, 1348-1360), it is shown that the resulting estimator has an oracle property with a proper choice of regu-larization parameters and penalty function. Simulation studies are conducted to illustrate the finite sample performances of the proposed procedures.
The Statistical Multifragmentation Model with Skyrme Effective Interactions
Souza, S R; Donangelo, R; Lynch, W G; Steiner, A W; Tsang, M B
2009-01-01
The Statistical Multifragmentation Model is modified to incorporate the Helmholtz free energies calculated in the finite temperature Thomas-Fermi approximation using Skyrme effective interactions. In this formulation, the density of the fragments at the freeze-out configuration corresponds to the equilibrium value obtained in the Thomas-Fermi approximation at the given temperature. The behavior of the nuclear caloric curve at constant volume is investigated in the micro-canonical ensemble and a plateau is observed for excitation energies between 8 and 10 MeV per nucleon. A kink in the caloric curve is found at the onset of this gas transition, indicating the existence of a small excitation energy region with negative heat capacity. In contrast to previous statistical calculations, this situation takes place even in this case in which the system is constrained to fixed volume. The observed phase transition takes place at approximately constant entropy. The charge distribution and other observables also turn ou...
The Statistical Multifragmentation Model with Skyrme Effective Interactions
Carlson, B V; Donangelo, R; Lynch, W G; Steiner, A W; Tsang, M B
2010-01-01
The Statistical Multifragmentation Model is modified to incorporate Helmholtz free energies calculated in the finite temperature Thomas-Fermi approximation using Skyrme effective interactions. In this formulation, the density of the fragments at the freeze-out configuration corresponds to the equilibrium value obtained in the Thomas-Fermi approximation at the given temperature. The behavior of the nuclear caloric curve, at constant volume, is investigated in the micro-canonical ensemble and a plateau is observed for excitation energies between 8 and 10 MeV per nucleon. A small kink in the caloric curve is found at the onset of this gas transition, indicating the existence of negative heat capacity, even in this case in which the system is constrained to a fixed volume, in contrast to former statistical calculations.
Modeling phenotypic plasticity in growth trajectories: a statistical framework.
Wang, Zhong; Pang, Xiaoming; Wu, Weimiao; Wang, Jianxin; Wang, Zuoheng; Wu, Rongling
2014-01-01
Phenotypic plasticity, that is multiple phenotypes produced by a single genotype in response to environmental change, has been thought to play an important role in evolution and speciation. Historically, knowledge about phenotypic plasticity has resulted from the analysis of static traits measured at a single time point. New insight into the adaptive nature of plasticity can be gained by an understanding of how organisms alter their developmental processes in a range of environments. Recent advances in statistical modeling of functional data and developmental genetics allow us to construct a dynamic framework of plastic response in developmental form and pattern. Under this framework, development, genetics, and evolution can be synthesized through statistical bridges to better address how evolution results from phenotypic variation in the process of development via genetic alterations.
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.
Statistical Process Control of a Kalman Filter Model
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A.
2014-01-01
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations. PMID:25264959
Statistical Process Control of a Kalman Filter Model
Directory of Open Access Journals (Sweden)
Sonja Gamse
2014-09-01
Full Text Available For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
A Statistical Quality Model for Data-Driven Speech Animation.
Ma, Xiaohan; Deng, Zhigang
2012-11-01
In recent years, data-driven speech animation approaches have achieved significant successes in terms of animation quality. However, how to automatically evaluate the realism of novel synthesized speech animations has been an important yet unsolved research problem. In this paper, we propose a novel statistical model (called SAQP) to automatically predict the quality of on-the-fly synthesized speech animations by various data-driven techniques. Its essential idea is to construct a phoneme-based, Speech Animation Trajectory Fitting (SATF) metric to describe speech animation synthesis errors and then build a statistical regression model to learn the association between the obtained SATF metric and the objective speech animation synthesis quality. Through delicately designed user studies, we evaluate the effectiveness and robustness of the proposed SAQP model. To the best of our knowledge, this work is the first-of-its-kind, quantitative quality model for data-driven speech animation. We believe it is the important first step to remove a critical technical barrier for applying data-driven speech animation techniques to numerous online or interactive talking avatar applications.
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.
Statistical process control of a Kalman filter model.
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A
2014-09-26
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
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.
Medical image segmentation using object atlas versus object cloud models
Phellan, Renzo; Falcão, Alexandre X.; Udupa, Jayaram K.
2015-03-01
Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.
Hybrid Perturbation methods based on Statistical Time Series models
San-Juan, Juan Félix; Pérez, Iván; López, Rosario
2016-01-01
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 a...
RM-structure alignment based statistical machine translation model
Institute of Scientific and Technical Information of China (English)
Sun Jiadong; Zhao Tiejun
2008-01-01
A novel model based on structure alignments is proposed for statistical machine translation in this paper.Meta-structure and sequence of meta-structure for a parse tree are defined.During the translation process, a parse tree is decomposed to deal with the structure divergence and the alignments can be constructed at different levels of recombination of meta-structure (RM).This method can perform the structure mapping across the sub-tree structure between languages.As a result, we get not only the translation for the target language, but sequence of meta-structure of its parse tree at the same time.Experiments show that the model in the framework of log-linear model has better generative ability and significantly outperforms Pharaoh, a phrase-based system.
STATISTICAL ANALYSIS OF THE TM- MODEL VIA BAYESIAN APPROACH
Directory of Open Access Journals (Sweden)
Muhammad Aslam
2012-11-01
Full Text Available The method of paired comparisons calls for the comparison of treatments presented in pairs to judges who prefer the better one based on their sensory evaluations. Thurstone (1927 and Mosteller (1951 employ the method of maximum likelihood to estimate the parameters of the Thurstone-Mosteller model for the paired comparisons. A Bayesian analysis of the said model using the non-informative reference (Jeffreys prior is presented in this study. The posterior estimates (means and joint modes of the parameters and the posterior probabilities comparing the two parameters are obtained for the analysis. The predictive probabilities that one treatment (Ti in preferred to any other treatment (Tj in a future single comparison are also computed. In addition, the graphs of the marginal posterior distributions of the individual parameter are drawn. The appropriateness of the model is also tested using the Chi-Square test statistic.
Dynamic statistical models of biological cognition: insights from communications theory
Wallace, Rodrick
2014-10-01
Maturana's cognitive perspective on the living state, Dretske's insight on how information theory constrains cognition, the Atlan/Cohen cognitive paradigm, and models of intelligence without representation, permit construction of a spectrum of dynamic necessary conditions statistical models of signal transduction, regulation, and metabolism at and across the many scales and levels of organisation of an organism and its context. Nonequilibrium critical phenomena analogous to physical phase transitions, driven by crosstalk, will be ubiquitous, representing not only signal switching, but the recruitment of underlying cognitive modules into tunable dynamic coalitions that address changing patterns of need and opportunity at all scales and levels of organisation. The models proposed here, while certainly providing much conceptual insight, should be most useful in the analysis of empirical data, much as are fitted regression equations.
Exploiting linkage disequilibrium in statistical modelling in quantitative genomics
DEFF Research Database (Denmark)
Wang, Lei
Alleles at two loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Genomic prediction and gene mapping rely on the existence of LD between gentic markers and causul variants of complex traits. In the first part of the thesis, a novel method...... to quantify and visualize local variation in LD along chromosomes in describet, and applied to characterize LD patters at the local and genome-wide scale in three Danish pig breeds. In the second part, different ways of taking LD into account in genomic prediction models are studied. One approach is to use...... the recently proposed antedependence models, which treat neighbouring marker effects as correlated; another approach involves use of haplotype block information derived using the program Beagle. The overall conclusion is that taking LD information into account in genomic prediction models potentially improves...
Modeling, dependence, classification, united statistical science, many cultures
Parzen, Emanuel
2012-01-01
Breiman (2001) proposed to statisticians awareness of two cultures: 1. Parametric modeling culture, pioneered by R.A.Fisher and Jerzy Neyman; 2. Algorithmic predictive culture, pioneered by machine learning research. Parzen (2001), as a part of discussing Breiman (2001), proposed that researchers be aware of many cultures, including the focus of our research: 3. Nonparametric, quantile based, information theoretic modeling. Our research seeks to unify statistical problem solving in terms of comparison density, copula density, measure of dependence, correlation, information, new measures (called LP score comoments) that apply to long tailed distributions with out finite second order moments. A very important goal is to unify methods for discrete and continuous random variables. We are actively developing these ideas, which have a history of many decades, since Parzen (1979, 1983) and Eubank et al. (1987). Our research extends these methods to modern high dimensional data modeling.
Discrete dynamical models: combinatorics, statistics and continuum approximations
Kornyak, Vladimir V
2015-01-01
This essay advocates the view that any problem that has a meaningful empirical content, can be formulated in constructive, more definitely, finite terms. We consider combinatorial models of dynamical systems and approaches to statistical description of such models. We demonstrate that many concepts of continuous physics --- such as continuous symmetries, the principle of least action, Lagrangians, deterministic evolution equations --- can be obtained from combinatorial structures as a result of the large number approximation. We propose a constructive description of quantum behavior that provides, in particular, a natural explanation of appearance of complex numbers in the formalism of quantum mechanics. Some approaches to construction of discrete models of quantum evolution that involve gauge connections are discussed.
Statistical mechanics of Monod-Wyman-Changeux (MWC) models.
Marzen, Sarah; Garcia, Hernan G; Phillips, Rob
2013-05-13
The 50th anniversary of the classic Monod-Wyman-Changeux (MWC) model provides an opportunity to survey the broader conceptual and quantitative implications of this quintessential biophysical model. With the use of statistical mechanics, the mathematical implementation of the MWC concept links problems that seem otherwise to have no ostensible biological connection including ligand-receptor binding, ligand-gated ion channels, chemotaxis, chromatin structure and gene regulation. Hence, a thorough mathematical analysis of the MWC model can illuminate the performance limits of a number of unrelated biological systems in one stroke. The goal of our review is twofold. First, we describe in detail the general physical principles that are used to derive the activity of MWC molecules as a function of their regulatory ligands. Second, we illustrate the power of ideas from information theory and dynamical systems for quantifying how well the output of MWC molecules tracks their sensory input, giving a sense of the "design" constraints faced by these receptors.
Symmetry Energy Effects in a Statistical Multifragmentation Model
Institute of Scientific and Technical Information of China (English)
ZHANG Lei; GAO Yuan1; ZHANG Hong-Fei; CHEN Xi-Meng; Yu Mei-Ling; LI Jun-Qing
2011-01-01
The symmetry energy effects on the nuclear disintegration mechanisms of the neutron-rich system (A0 = 200, Z0 = 78) are studied in the framework of the statistical multifragmentation model (SMM) within its micro-canonical ensemble. A modified symmetry energy term with consideration of the volume and surface asymmetry is adopted instead of the original invariable value in the standard SMM model. The results indicate that as the volume and surface asymmetries are considered, the neutron-rich system translates to a fission-like process from evaporation earlier than the original standard SMM model at lower excitation energies, and its mass distribution has larger probabilities in the medium-heavy nuclei range so that the system breaks up more averagely. When the excitation energy becomes higher, the volume and surface asymmetry lead to a smaller average multiplicity.%The symmetry energy effects on the nuclear disintegration mechanisms of the neutron-rich system (A0 =200,Z0 =78) are studied in the framework of the statistical multifragmentation model (SMM) within its micro-canonical ensemble.A modified symmetry energy term with consideration of the volume and surface asymmetry is adopted instead of the original invariable value in the standard SMM model.The results indicate that as the volume and surface asymmetries are considered,the neutron-rich system translates to a fission-like process from evaporation earlier than the original standard SMM model at lower excitation energies,and its mass distribution has larger probabilities in the medium-heavy nuclei range so that the system breaks up more averagely.When the excitation energy becomes higher,the volume and surface asymmetry lead to a smaller average multiplicity.
Masked areas in shear peak statistics. A forward modeling approach
Energy Technology Data Exchange (ETDEWEB)
Bard, D.; Kratochvil, J. M.; Dawson, W.
2016-03-09
The statistics of shear peaks have been shown to provide valuable cosmological information beyond the power spectrum, and will be an important constraint of models of cosmology in forthcoming astronomical surveys. Surveys include masked areas due to bright stars, bad pixels etc., which must be accounted for in producing constraints on cosmology from shear maps. We advocate a forward-modeling approach, where the impacts of masking and other survey artifacts are accounted for in the theoretical prediction of cosmological parameters, rather than correcting survey data to remove them. We use masks based on the Deep Lens Survey, and explore the impact of up to 37% of the survey area being masked on LSST and DES-scale surveys. By reconstructing maps of aperture mass the masking effect is smoothed out, resulting in up to 14% smaller statistical uncertainties compared to simply reducing the survey area by the masked area. We show that, even in the presence of large survey masks, the bias in cosmological parameter estimation produced in the forward-modeling process is ≈1%, dominated by bias caused by limited simulation volume. We also explore how this potential bias scales with survey area and evaluate how much small survey areas are impacted by the differences in cosmological structure in the data and simulated volumes, due to cosmic variance.
Statistical Inference for Point Process Models of Rainfall
Smith, James A.; Karr, Alan F.
1985-01-01
In this paper we develop maximum likelihood procedures for parameter estimation and model selection that apply to a large class of point process models that have been used to model rainfall occurrences, including Cox processes, Neyman-Scott processes, and renewal processes. The statistical inference procedures are based on the stochastic intensity λ(t) = lims→0,s>0 (1/s)E[N(t + s) - N(t)|N(u), u process is shown to have a simple expression in terms of the stochastic intensity. The main result of this paper is a recursive procedure for computing stochastic intensities; the procedure is applicable to a broad class of point process models, including renewal Cox process with Markovian intensity processes and an important class of Neyman-Scott processes. The model selection procedure we propose, which is based on likelihood ratios, allows direct comparison of two classes of point processes to determine which provides a better model for a given data set. The estimation and model selection procedures are applied to two data sets of simulated Cox process arrivals and a data set of daily rainfall occurrences in the Potomac River basin.
How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
du Plessis, Louis; Leventhal, Gabriel E.; Bonhoeffer, Sebastian
2016-01-01
Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations. PMID:27189564
How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
du Plessis, Louis; Leventhal, Gabriel E; Bonhoeffer, Sebastian
2016-09-01
Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.
Random matrices as models for the statistics of quantum mechanics
Casati, Giulio; Guarneri, Italo; Mantica, Giorgio
1986-05-01
Random matrices from the Gaussian unitary ensemble generate in a natural way unitary groups of evolution in finite-dimensional spaces. The statistical properties of this time evolution can be investigated by studying the time autocorrelation functions of dynamical variables. We prove general results on the decay properties of such autocorrelation functions in the limit of infinite-dimensional matrices. We discuss the relevance of random matrices as models for the dynamics of quantum systems that are chaotic in the classical limit. Permanent address: Dipartimento di Fisica, Via Celoria 16, 20133 Milano, Italy.
Stochastical modeling for Viral Disease: Statistical Mechanics and Network Theory
Zhou, Hao; Deem, Michael
2007-04-01
Theoretical methods of statistical mechanics are developed and applied to study the immunological response against viral disease, such as dengue. We use this theory to show how the immune response to four different dengue serotypes may be sculpted. It is the ability of avian influenza, to change and to mix, that has given rise to the fear of a new human flu pandemic. Here we propose to utilize a scale free network based stochastic model to investigate the mitigation strategies and analyze the risk.
Efficiency of a statistical transport model for turbulent particle dispersion
Litchford, Ron J.; Jeng, San-Mou
1992-01-01
In developing its theory for turbulent dispersion transport, the Litchford and Jeng (1991) statistical transport model for turbulent particle dispersion took a generalized approach in which the perturbing influence of each turbulent eddy on consequent interactions was transported through all subsequent eddies. Nevertheless, examinations of this transport relation shows it to be able to decay rapidly: this implies that additional computational efficiency may be obtained via truncation of unneccessary transport terms. Attention is here given to the criterion for truncation, as well as to expected efficiency gains.
Social inequality: from data to statistical physics modeling
Chatterjee, Arnab; Ghosh, Asim; Inoue, Jun-ichi; Chakrabarti, Bikas K.
2015-09-01
Social inequality is a topic of interest since ages, and has attracted researchers across disciplines to ponder over it origin, manifestation, characteristics, consequences, and finally, the question of how to cope with it. It is manifested across different strata of human existence, and is quantified in several ways. In this review we discuss the origins of social inequality, the historical and commonly used non-entropic measures such as Lorenz curve, Gini index and the recently introduced k index. We also discuss some analytical tools that aid in understanding and characterizing them. Finally, we argue how statistical physics modeling helps in reproducing the results and interpreting them.
Social inequality: from data to statistical physics modeling
Chatterjee, Arnab; Inoue, Jun-ichi; Chakrabarti, Bikas K
2015-01-01
Social inequality is a topic of interest since ages, and has attracted researchers across disciplines to ponder over it origin, manifestation, characteristics, consequences, and finally, the question of how to cope with it. It is manifested across different strata of human existence, and is quantified in several ways. In this review we discuss the origins of social inequality, the historical and commonly used non-entropic measures such as Lorenz curve, Gini index and the recently introduced $k$ index. We also discuss some analytical tools that aid in understanding and characterizing them. Finally, we argue how statistical physics modeling helps in reproducing the results and interpreting them.
A Probabilistic Rain Diagnostic Model Based on Cyclone Statistical Analysis
Iordanidou, V.; A. G. Koutroulis; I. K. Tsanis
2014-01-01
Data from a dense network of 69 daily precipitation gauges over the island of Crete and cyclone climatological analysis over middle-eastern Mediterranean are combined in a statistical approach to develop a rain diagnostic model. Regarding the dataset, 0.5 × 0.5, 33-year (1979–2011) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) is used. The cyclone tracks and their characteristics are identified with the aid of Melbourne University algorithm (MS scheme). T...
DEFF Research Database (Denmark)
Fournier, David A.; Skaug, Hans J.; Ancheta, Johnoel
2011-01-01
Many criteria for statistical parameter estimation, such as maximum likelihood, are formulated as a nonlinear optimization problem.Automatic Differentiation Model Builder (ADMB) is a programming framework based on automatic differentiation, aimed at highly nonlinear models with a large number...
Statistical modeling and visualization of localized prostate cancer
Wang, Yue J.; Xuan, Jianhua; Sesterhenn, Isabell A.; Hayes, Wendelin S.; Ebert, David S.; Lynch, John H.; Mun, Seong K.
1997-05-01
In this paper, a statistically significant master model of localized prostate cancer is developed with pathologically- proven surgical specimens to spatially guide specific points in the biopsy technique for a higher rate of prostate cancer detection and the best possible representation of tumor grade and extension. Based on 200 surgical specimens of the prostates, we have developed a surface reconstruction technique to interactively visualize in the clinically significant objects of interest such as the prostate capsule, urethra, seminal vesicles, ejaculatory ducts and the different carcinomas, for each of these cases. In order to investigate the complex disease pattern including the tumor distribution, volume, and multicentricity, we created a statistically significant master model of localized prostate cancer by fusing these reconstructed computer models together, followed by a quantitative formulation of the 3D finite mixture distribution. Based on the reconstructed prostate capsule and internal structures, we have developed a technique to align all surgical specimens through elastic matching. By labeling the voxels of localized prostate cancer by '1' and the voxels of other internal structures by '0', we can generate a 3D binary image of the prostate that is simply a mutually exclusive random sampling of the underlying distribution f cancer to gram of localized prostate cancer characteristics. In order to quantify the key parameters such as distribution, multicentricity, and volume, we used a finite generalized Gaussian mixture to model the histogram, and estimate the parameter values through information theoretical criteria and a probabilistic self-organizing mixture. Utilizing minimally-immersive and stereoscopic interactive visualization, an augmented reality can be developed to allow the physician to virtually hold the master model in one hand and use the dominant hand to probe data values and perform a simulated needle biopsy. An adaptive self- organizing
An Extended Clustering Algorithm for Statistical Language Models
Ueberla, J P
1994-01-01
Statistical language models frequently suffer from a lack of training data. This problem can be alleviated by clustering, because it reduces the number of free parameters that need to be trained. However, clustered models have the following drawback: if there is ``enough'' data to train an unclustered model, then the clustered variant may perform worse. On currently used language modeling corpora, e.g. the Wall Street Journal corpus, how do the performances of a clustered and an unclustered model compare? While trying to address this question, we develop the following two ideas. First, to get a clustering algorithm with potentially high performance, an existing algorithm is extended to deal with higher order N-grams. Second, to make it possible to cluster large amounts of training data more efficiently, a heuristic to speed up the algorithm is presented. The resulting clustering algorithm can be used to cluster trigrams on the Wall Street Journal corpus and the language models it produces can compete with exi...
An efficient simulator of 454 data using configurable statistical models
Directory of Open Access Journals (Sweden)
Persson Bengt
2011-10-01
Full Text Available Abstract Background Roche 454 is one of the major 2nd generation sequencing platforms. The particular characteristics of 454 sequence data pose new challenges for bioinformatic analyses, e.g. assembly and alignment search algorithms. Simulation of these data is therefore useful, in order to further assess how bioinformatic applications and algorithms handle 454 data. Findings We developed a new application named 454sim for simulation of 454 data at high speed and accuracy. The program is multi-thread capable and is available as C++ source code or pre-compiled binaries. Sequence reads are simulated by 454sim using a set of statistical models for each chemistry. 454sim simulates recorded peak intensities, peak quality deterioration and it calculates quality values. All three generations of the Roche 454 chemistry ('GS20', 'GS FLX' and 'Titanium' are supported and defined in external text files for easy access and tweaking. Conclusions We present a new platform independent application named 454sim. 454sim is generally 200 times faster compared to previous programs and it allows for simple adjustments of the statistical models. These improvements make it possible to carry out more complex and rigorous algorithm evaluations in a reasonable time scale.
Energy Level Statistics in Particle—Rotor Model
Institute of Scientific and Technical Information of China (English)
ZHOUXian－Rong; MENGJie; 等
2002-01-01
Energy level statistics of a system consisting of six particles interacting by delta force in a two-j model coupled with a deformed core is studied in particle-rotor model.For single-j shell (i13/2) and two-j shell (g7/2+d5/2) the exact energies for our statistical analysis are obtained from a full diagonalization of the Hamiltonian,whilt in two-j case (i13/2+g9/2) the configuration truncation is used.The nearest-neighbor distribution of energy levels and spectral rigidity are studied as the function of spin.The results of single-j shell are compared with those in two-j case.It is showed that the system becomes more regular when single-j space (i13/2) is replaced by two-j shell (g7/2+d5/2) although the basis size of the configuration space is unchanged.The degree of chaoticity of the system,however,changes slightly when configuration space is enlarged by extending single-j shell (i13/2) to two-j shell (i13/2+g9/2).
Statistic Model Based Dynamic Channel Compensation for Telephony Speech Recognition
Institute of Scientific and Technical Information of China (English)
ZHANGHuayun; HANZhaobing; XUBo
2004-01-01
The degradation of speech recognition performance in real-life environments and through transmission channels is a main embarrassment for many speechbased applications around the world, especially when nonstationary noise and changing channel exist. Previous works have shown that the main reason for this performance degradation is the variational mismatch caused by different telephone channels between the testing and training sets. In this paper, we propose a statistic model based implementation to dynamically compensate this mismatch. Firstly, we focus on a Maximum-likelihood (ML) estimation algorithm for telephone channels. In experiments on Mandarin Large vocabulary continuous speech recognition (LVCSR) over telephone lines, the Character error rate (CER) decreases more than 20%. The average delay is about 300-400ms. Secondly, we will extend it by introducing a phone-conditioned prior statistic model for the channels and applying Maximum a posteriori (MAP) estimation technique. Compared to the ML based method, the MAP based algorithm follows with the variations within channels more effectively. Average delay of the algorithm is decreased to 200ms. An additional 7-8% CER relative reduction is observed in LVCSR.
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.
Isospin dependence of nuclear multifragmentation in statistical model
Institute of Scientific and Technical Information of China (English)
ZHANG Lei; XIE Dong-Zhu; ZHANG Yan-Ping; GAO Yuan
2011-01-01
The evolution of nuclear disintegration mechanisms with increasing excitation energy, from com- pound nucleus to multifragmentation, has been studied by using the Statistical Multifragmentation Model (SMM) within a micro-canonical ensemble. We discuss the observable characteristics as functions of excitation energy in multifragmentation, concentrating on the isospin dependence of the model in its decaying mechanism and break-up fragment configuration by comparing the A = 200, Z = 78 and A = 200, Z = 100 systems. The calculations indicate that the neutron-rich system (Z = 78) translates to a fission-like process from evaporation later than the symmetric nucleus at a lower excitation energy, but gets a larger average multiplicity as the excitation energy increases above 1.0 MeV/u.
Image Watermarking Using Visual Perception Model and Statistical Features
Directory of Open Access Journals (Sweden)
Mrs.C.Akila
2010-06-01
Full Text Available This paper presents an effective method for the image watermarking using visual perception model based on statistical features in the low frequency domain. In the image watermarking community watermark resistance to geometric attacks is an important issue. Most countermeasures proposed in the literature usually focus on the problem of global affine transforms such as rotation, scaling and translation (RST, but few are resistant to challenging cropping and random bending attacks (RBAs. Normally in the case of watermarking there may be an occurrence of distortion in the form of artifacts. A visual perception model is proposed to quantify the localized tolerance to noise for arbitrary imagery which achieves the reduction of artifacts. As a result, the watermarking system provides a satisfactory performance for those content-preserving geometric deformations and image processing operations, including JPEG ompression, low pass filtering, cropping and RBAs.
Role of scaling in the statistical modelling of finance
Indian Academy of Sciences (India)
Attilio L Stella; Fulvio Baldovin
2008-08-01
Modelling the evolution of a financial index as a stochastic process is a problem awaiting a full, satisfactory solution since it was first formulated by Bachelier in 1900. Here it is shown that the scaling with time of the return probability density function sampled from the historical series suggests a successful model. The resulting stochastic process is a heteroskedastic, non-Markovian martingale, which can be used to simulate index evolution on the basis of an autoregressive strategy. Results are fully consistent with volatility clustering and with the multiscaling properties of the return distribution. The idea of basing the process construction on scaling, and the construction itself, are closely inspired by the probabilistic renormalization group approach of statistical mechanics and by a recent formulation of the central limit theorem for sums of strongly correlated random variables.
Helicity statistics in homogeneous and isotropic turbulence and turbulence models
Sahoo, Ganapati; Biferale, Luca
2016-01-01
We study the statistical properties of helicity in direct numerical simulations of fully developed homogeneous and isotropic turbulence and in a class of turbulence shell models. We consider correlation functions based on combinations of vorticity and velocity increments that are not invariant under mirror symmetry. We also study the scaling properties of high-order structure functions based on the moments of the velocity increments projected on a subset of modes with either positive or negative helicity (chirality). We show that mirror symmetry is recovered at small-scales, i.e. chiral terms are always subleading and they are well captured by a dimensional argument plus a small anomalous correction. We confirm these findings with numerical study of helical shell models at high Reynolds numbers.
Helicity statistics in homogeneous and isotropic turbulence and turbulence models
Sahoo, Ganapati; De Pietro, Massimo; Biferale, Luca
2017-02-01
We study the statistical properties of helicity in direct numerical simulations of fully developed homogeneous and isotropic turbulence and in a class of turbulence shell models. We consider correlation functions based on combinations of vorticity and velocity increments that are not invariant under mirror symmetry. We also study the scaling properties of high-order structure functions based on the moments of the velocity increments projected on a subset of modes with either positive or negative helicity (chirality). We show that mirror symmetry is recovered at small scales, i.e., chiral terms are subleading and they are well captured by a dimensional argument plus anomalous corrections. These findings are also supported by a high Reynolds numbers study of helical shell models with the same chiral symmetry of Navier-Stokes equations.
Statistical Agent Based Modelization of the Phenomenon of Drug Abuse
di Clemente, Riccardo; Pietronero, Luciano
2012-07-01
We introduce a statistical agent based model to describe the phenomenon of drug abuse and its dynamical evolution at the individual and global level. The agents are heterogeneous with respect to their intrinsic inclination to drugs, to their budget attitude and social environment. The various levels of drug use were inspired by the professional description of the phenomenon and this permits a direct comparison with all available data. We show that certain elements have a great importance to start the use of drugs, for example the rare events in the personal experiences which permit to overcame the barrier of drug use occasionally. The analysis of how the system reacts to perturbations is very important to understand its key elements and it provides strategies for effective policy making. The present model represents the first step of a realistic description of this phenomenon and can be easily generalized in various directions.
Statistical Agent Based Modelization of the Phenomenon of Drug Abuse
Di Clemente, Riccardo; 10.1038/srep00532
2012-01-01
We introduce a statistical agent based model to describe the phenomenon of drug abuse and its dynamical evolution at the individual and global level. The agents are heterogeneous with respect to their intrinsic inclination to drugs, to their budget attitude and social environment. The various levels of drug use were inspired by the professional description of the phenomenon and this permits a direct comparison with all available data. We show that certain elements have a great importance to start the use of drugs, for example the rare events in the personal experiences which permit to overcame the barrier of drug use occasionally. The analysis of how the system reacts to perturbations is very important to understand its key elements and it provides strategies for effective policy making. The present model represents the first step of a realistic description of this phenomenon and can be easily generalized in various directions.
Statistical properties of cloud lifecycles in cloud-resolving models
Directory of Open Access Journals (Sweden)
R. S. Plant
2008-12-01
Full Text Available A new technique is described for the analysis of cloud-resolving model simulations, which allows one to investigate the statistics of the lifecycles of cumulus clouds. Clouds are tracked from timestep-to-timestep within the model run. This allows for a very simple method of tracking, but one which is both comprehensive and robust. An approach for handling cloud splits and mergers is described which allows clouds with simple and complicated time histories to be compared within a single framework. This is found to be important for the analysis of an idealized simulation of radiative-convective equilibrium, in which the moist, buoyant, updrafts (i.e., the convective cores were tracked. Around half of all such cores were subject to splits and mergers during their lifecycles. For cores without any such events, the average lifetime is 30 min, but events can lengthen the typical lifetime considerably.
Statistical analysis and model of spread F occurrence in China
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The spread F data obtained over Lanzhou (36.1°N,103.9°E),Chongqing (29.5°N,106.4°E) and Haikou (20.0°N,110.3°E) of China during the period from 1978 to 1997 are used to analyze the occurrence characteristics.The statistical results show that the post midnight spread F occurrence is maximum during the summer solstice months of the lower solar activity period,while post sunset spread F is dominant in equinoxes of higher solar activity period over Haikou station.Over Chongqing and Lanzhou stations,spread F mostly occurs at post midnight and relates negatively with solar activity.Using regression method and Fourier expansion,the preliminary single-station model of spread F occurrence is established and the accuracy of the model is evaluated.
Statistical Modeling of Robotic Random Walks on Different Terrain
Naylor, Austin; Kinnaman, Laura
Issues of public safety, especially with crowd dynamics and pedestrian movement, have been modeled by physicists using methods from statistical mechanics over the last few years. Complex decision making of humans moving on different terrains can be modeled using random walks (RW) and correlated random walks (CRW). The effect of different terrains, such as a constant increasing slope, on RW and CRW was explored. LEGO robots were programmed to make RW and CRW with uniform step sizes. Level ground tests demonstrated that the robots had the expected step size distribution and correlation angles (for CRW). The mean square displacement was calculated for each RW and CRW on different terrains and matched expected trends. The step size distribution was determined to change based on the terrain; theoretical predictions for the step size distribution were made for various simple terrains. It's Dr. Laura Kinnaman, not sure where to put the Prefix.
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.
Methods of artificial enlargement of the training set for statistical shape models.
Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki
2008-11-01
Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.
Botvina, A; Gupta, S Das; Mishustin, I
2008-01-01
The statistical multifragmentation model (SMM) has been widely used to explain experimental data of intermediate energy heavy ion collisions. A later entrant in the field is the canonical thermodynamic model (CTM) which is also being used to fit experimental data. The basic physics of both the models is the same, namely that fragments are produced according to their statistical weights in the available phase space. However, they are based on different statistical ensembles, and the methods of calculation are different: while the SMM uses Monte-Carlo simulations, the CTM solves recursion relations. In this paper we compare the predictions of the two models for a few representative cases.
A statistically predictive model for future monsoon failure in India
Schewe, Jacob; Levermann, Anders
2012-12-01
Indian monsoon rainfall is vital for a large share of the world’s population. Both reliably projecting India’s future precipitation and unraveling abrupt cessations of monsoon rainfall found in paleorecords require improved understanding of its stability properties. While details of monsoon circulations and the associated rainfall are complex, full-season failure is dominated by large-scale positive feedbacks within the region. Here we find that in a comprehensive climate model, monsoon failure is possible but very rare under pre-industrial conditions, while under future warming it becomes much more frequent. We identify the fundamental intraseasonal feedbacks that are responsible for monsoon failure in the climate model, relate these to observational data, and build a statistically predictive model for such failure. This model provides a simple dynamical explanation for future changes in the frequency distribution of seasonal mean all-Indian rainfall. Forced only by global mean temperature and the strength of the Pacific Walker circulation in spring, it reproduces the trend as well as the multidecadal variability in the mean and skewness of the distribution, as found in the climate model. The approach offers an alternative perspective on large-scale monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions.
Modelling medical care usage under medical insurance scheme for urban non-working residents.
Xiong, Linping; Tian, Wenhua; Tang, Weidong
2013-06-01
This research investigates and evaluates China's urban medical care usage for non-working residents using microsimulation techniques. It focuses on modelling medical services usage and simulating medical expenses on hospitalization treatments as well as clinic services for serious illness in an urban area for the period of 2008-2010. A static microsimulation model was created to project the impact of the medical insurance scheme. Four kinds of achievements have been made. For three different scenarios, the model predicted the hospitalization services costs and payments, as well as the balance of the social pool fund and the medical burden on families.
Flashover of a vacuum-insulator interface: A statistical model
Directory of Open Access Journals (Sweden)
W. A. Stygar
2004-07-01
Full Text Available We have developed a statistical model for the flashover of a 45° vacuum-insulator interface (such as would be found in an accelerator subject to a pulsed electric field. The model assumes that the initiation of a flashover plasma is a stochastic process, that the characteristic statistical component of the flashover delay time is much greater than the plasma formative time, and that the average rate at which flashovers occur is a power-law function of the instantaneous value of the electric field. Under these conditions, we find that the flashover probability is given by 1-exp(-E_{p}^{β}t_{eff}C/k^{β}, where E_{p} is the peak value in time of the spatially averaged electric field E(t, t_{eff}≡∫[E(t/E_{p}]^{β}dt is the effective pulse width, C is the insulator circumference, k∝exp(λ/d, and β and λ are constants. We define E(t as V(t/d, where V(t is the voltage across the insulator and d is the insulator thickness. Since the model assumes that flashovers occur at random azimuthal locations along the insulator, it does not apply to systems that have a significant defect, i.e., a location contaminated with debris or compromised by an imperfection at which flashovers repeatedly take place, and which prevents a random spatial distribution. The model is consistent with flashover measurements to within 7% for pulse widths between 0.5 ns and 10 μs, and to within a factor of 2 between 0.5 ns and 90 s (a span of over 11 orders of magnitude. For these measurements, E_{p} ranges from 64 to 651 kV/cm, d from 0.50 to 4.32 cm, and C from 4.96 to 95.74 cm. The model is significantly more accurate, and is valid over a wider range of parameters, than the J. C. Martin flashover relation that has been in use since 1971 [J. C. Martin on Pulsed Power, edited by T. H. Martin, A. H. Guenther, and M. Kristiansen (Plenum, New York, 1996]. We have generalized the statistical model to estimate the total-flashover probability of an
Energy Technology Data Exchange (ETDEWEB)
Teixeira, Marilia S.; Pinto, Nivia G.P.; Barroso, Regina C.; Oliveira, Luis F., E-mail: mariliasilvat@gmail.co, E-mail: lfolive@oi.com.b, E-mail: cely_barroso@hotmail.co, E-mail: nitatag@gmail.co [Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ (Brazil). Inst. de Fisica
2009-07-01
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
A Statistical Model for Regional Tornado Climate Studies.
Jagger, Thomas H; Elsner, James B; Widen, Holly M
2015-01-01
Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.
A Statistical Model for Regional Tornado Climate Studies.
Directory of Open Access Journals (Sweden)
Thomas H Jagger
Full Text Available Tornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA. A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.
A Generalized Statistical Uncertainty Model for Satellite Precipitation Products
Sarachi, S.
2013-12-01
A mixture model of Generalized Normal Distribution and Gamma distribution (GND-G) is used to model the joint probability distribution of satellite-based and stage IV radar rainfall under a given spatial and temporal resolution (e.g. 1°x1° and daily rainfall). The distribution parameters of GND-G are extended across various rainfall rates and spatial and temporal resolutions. In the study, GND-G is used to describe the uncertainty of the estimates from Precipitation Estimation from Remote Sensing Information using Artificial Neural Network algorithm (PERSIANN). The stage IV-based multi-sensor precipitation estimates (MPE) are used as reference measurements .The study area for constructing the uncertainty model covers a 15°×15°box of 0.25°×0.25° cells over the eastern United States for summer 2004 to 2009. Cells are aggregated in space and time to obtain data with different resolutions for the construction of the model's parameter space. Result shows that comparing to the other statistical uncertainty models, GND-G fits better than the other models, such as Gaussian and Gamma distributions, to the reference precipitation data. The impact of precipitation uncertainty to the stream flow is further demonstrated by Monte Carlo simulation of precipitation forcing in the hydrologic model. The NWS DMIP2 basins over Illinois River basin south of Siloam is selected in this case study. The data covers the time period of 2006 to 2008.The uncertainty range of stream flow from precipitation of GND-G distributions calculated and will be discussed.
Emerging Trends and Statistical Analysis in Computational Modeling in Agriculture
Directory of Open Access Journals (Sweden)
Sunil Kumar
2015-03-01
Full Text Available In this paper the authors have tried to describe emerging trend in computational modelling used in the sphere of agriculture. Agricultural computational modelling with the use of intelligence techniques for computing the agricultural output by providing minimum input data to lessen the time through cutting down the multi locational field trials and also the labours and other inputs is getting momentum. Development of locally suitable integrated farming systems (IFS is the utmost need of the day, particularly in India where about 95% farms are under small and marginal holding size. Optimization of the size and number of the various enterprises to the desired IFS model for a particular set of agro-climate is essential components of the research to sustain the agricultural productivity for not only filling the stomach of the bourgeoning population of the country, but also to enhance the nutritional security and farms return for quality life. Review of literature pertaining to emerging trends in computational modelling applied in field of agriculture is done and described below for the purpose of understanding its trends mechanism behavior and its applications. Computational modelling is increasingly effective for designing and analysis of the system. Computa-tional modelling is an important tool to analyses the effect of different scenarios of climate and management options on the farming systems and its interaction among themselves. Further, authors have also highlighted the applications of computational modeling in integrated farming system, crops, weather, soil, climate, horticulture and statistical used in agriculture which can show the path to the agriculture researcher and rural farming community to replace some of the traditional techniques.
Testing the DGP model with gravitational lensing statistics
Zhu, Zong-Hong; Sereno, M.
2008-09-01
Aims: The self-accelerating braneworld model (DGP) appears to provide a simple alternative to the standard ΛCDM cosmology to explain the current cosmic acceleration, which is strongly indicated by measurements of type Ia supernovae, as well as other concordant observations. Methods: We investigate observational constraints on this scenario provided by gravitational-lensing statistics using the Cosmic Lens All-Sky Survey (CLASS) lensing sample. Results: We show that a substantial part of the parameter space of the DGP model agrees well with that of radio source gravitational lensing sample. Conclusions: In the flat case, Ω_K=0, the likelihood is maximized, L=L_max, for ΩM = 0.30-0.11+0.19. If we relax the prior on Ω_K, the likelihood peaks at Ω_M,Ωr_c ≃ 0.29, 0.12, slightly in the region of open models. The confidence contours are, however, elongated such that we are unable to discard any of the close, flat or open models.
A hybrid random field model for scalable statistical learning.
Freno, A; Trentin, E; Gori, M
2009-01-01
This paper introduces hybrid random fields, which are a class of probabilistic graphical models aimed at allowing for efficient structure learning in high-dimensional domains. Hybrid random fields, along with the learning algorithm we develop for them, are especially useful as a pseudo-likelihood estimation technique (rather than a technique for estimating strict joint probability distributions). In order to assess the generality of the proposed model, we prove that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. Once we establish this result, we develop a scalable algorithm for learning the structure of hybrid random fields, which we call 'Markov Blanket Merging'. On the one hand, we characterize some complexity properties of Markov Blanket Merging both from a theoretical and from the experimental point of view, using a series of synthetic benchmarks. On the other hand, we evaluate the accuracy of hybrid random fields (as learned via Markov Blanket Merging) by comparing them to various alternative statistical models in a number of pattern classification and link-prediction applications. As the results show, learning hybrid random fields by the Markov Blanket Merging algorithm not only reduces significantly the computational cost of structure learning with respect to several considered alternatives, but it also leads to models that are highly accurate as compared to the alternative ones.
Linear mixed models a practical guide using statistical software
West, Brady T; Galecki, Andrzej T
2014-01-01
Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM.New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggest...
Glass viscosity calculation based on a global statistical modelling approach
Energy Technology Data Exchange (ETDEWEB)
Fluegel, Alex
2007-02-01
A global statistical glass viscosity model was developed for predicting the complete viscosity curve, based on more than 2200 composition-property data of silicate glasses from the scientific literature, including soda-lime-silica container and float glasses, TV panel glasses, borosilicate fiber wool and E type glasses, low expansion borosilicate glasses, glasses for nuclear waste vitrification, lead crystal glasses, binary alkali silicates, and various further compositions from over half a century. It is shown that within a measurement series from a specific laboratory the reported viscosity values are often over-estimated at higher temperatures due to alkali and boron oxide evaporation during the measurement and glass preparation, including data by Lakatos et al. (1972) and the recently published High temperature glass melt property database for process modeling by Seward et al. (2005). Similarly, in the glass transition range many experimental data of borosilicate glasses are reported too high due to phase separation effects. The developed global model corrects those errors. The model standard error was 9-17°C, with R^2 = 0.985-0.989. The prediction 95% confidence interval for glass in mass production largely depends on the glass composition of interest, the composition uncertainty, and the viscosity level. New insights in the mixed-alkali effect are provided.
Improving statistical forecasts of seasonal streamflows using hydrological model output
Directory of Open Access Journals (Sweden)
D. E. Robertson
2013-02-01
Full Text Available Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1 when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2 when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3 when the initial catchment condition is near saturation intermittently throughout the historical record.
Improving statistical forecasts of seasonal streamflows using hydrological model output
Robertson, D. E.; Pokhrel, P.; Wang, Q. J.
2013-02-01
Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.
A Statistical Toolbox For Mining And Modeling Spatial Data
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D’Aubigny Gérard
2016-12-01
Full Text Available Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP, valuable in exploratory spatial data analysis.
Energy Level Statistics in Particle-Rotor Model
Institute of Scientific and Technical Information of China (English)
ZHOU Xian-Rong; GUO Lu; MENG Jie; ZHAO En-Guang
2002-01-01
Energy level statistics of a system consisting of six particles interacting by delta force in a two-j modelcoupled with a deformed core is studied in particle-rotor model. For single-j shell (i13/2) and two-j shell (g7/2 + d5/2)the exact energies for our statistical analysis are obtained from a full diagonalization of the Hamiltonian, while in two-jcase (i13/2 + g9/2) the configuration truncation is used. The nearest-neighbor distribution of energy levels and spectralrigidity are studied as the function of spin. The results of single-j shell are compared with those in two-j case. It isshowed that the system becomes more regular when single-j space (i13/2) is replaced by two-j shell (g7/2 +d5/2) althoughthe basis size of the configuration space is unchanged. The degree of chaoticity of the system, however, changes slightlywhen configuration space is enlarged by extending single-j shell (i13/2) to two-j shell (i13/2 + g9/2).
Langousis, Andreas; Mamalakis, Antonios; Deidda, Roberto; Marrocu, Marino
2016-01-01
To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris () developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical downscaling scheme of Langousis and Kaleris (), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the ENSEMBLES project. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima.
STATISTICAL MECHANICS MODELING OF MESOSCALE DEFORMATION IN METALS
Energy Technology Data Exchange (ETDEWEB)
Anter El-Azab
2013-04-08
The research under this project focused on a theoretical and computational modeling of dislocation dynamics of mesoscale deformation of metal single crystals. Specifically, the work aimed to implement a continuum statistical theory of dislocations to understand strain hardening and cell structure formation under monotonic loading. These aspects of crystal deformation are manifestations of the evolution of the underlying dislocation system under mechanical loading. The project had three research tasks: 1) Investigating the statistical characteristics of dislocation systems in deformed crystals. 2) Formulating kinetic equations of dislocations and coupling these kinetics equations and crystal mechanics. 3) Computational solution of coupled crystal mechanics and dislocation kinetics. Comparison of dislocation dynamics predictions with experimental results in the area of statistical properties of dislocations and their field was also a part of the proposed effort. In the first research task, the dislocation dynamics simulation method was used to investigate the spatial, orientation, velocity, and temporal statistics of dynamical dislocation systems, and on the use of the results from this investigation to complete the kinetic description of dislocations. The second task focused on completing the formulation of a kinetic theory of dislocations that respects the discrete nature of crystallographic slip and the physics of dislocation motion and dislocation interaction in the crystal. Part of this effort also targeted the theoretical basis for establishing the connection between discrete and continuum representation of dislocations and the analysis of discrete dislocation simulation results within the continuum framework. This part of the research enables the enrichment of the kinetic description with information representing the discrete dislocation systems behavior. The third task focused on the development of physics-inspired numerical methods of solution of the coupled
Luo, Li; Cheng, Xiaohua; Wang, Shiyuan; Zhang, Junxue; Zhu, Wenbo; Yang, Jiaying; Liu, Pei
2017-09-19
Blended learning that combines a modular object-oriented dynamic learning environment (Moodle) with face-to-face teaching was applied to a medical statistics course to improve learning outcomes and evaluate the impact factors of students' knowledge, attitudes and practices (KAP) relating to e-learning. The same real-name questionnaire was administered before and after the intervention. The summed scores of every part (knowledge, attitude and practice) were calculated using the entropy method. A mixed linear model was fitted using the SAS PROC MIXED procedure to analyse the impact factors of KAP. Educational reform, self-perceived character, registered permanent residence and hours spent online per day were significant impact factors of e-learning knowledge. Introversion and middle type respondents' average scores were higher than those of extroversion type respondents. Regarding e-learning attitudes, educational reform, community number, Internet age and hours spent online per day had a significant impact. Specifically, participants whose Internet age was no greater than 6 years scored 7.00 points lower than those whose Internet age was greater than 10 years. Regarding e-learning behaviour, educational reform and parents' literacy had a significant impact, as the average score increased 10.05 points (P learning KAP. Additionally, this type of blended course can be implemented in many other curriculums.
A statistical downscaling model for summer rainfall over Pakistan
Kazmi, Dildar Hussain; Li, Jianping; Ruan, Chengqing; Zhao, Sen; Li, Yanjie
2016-10-01
A statistical approach is utilized to construct an interannual model for summer (July-August) rainfall over the western parts of South Asian Monsoon. Observed monthly rainfall data for selected stations of Pakistan for the last 55 years (1960-2014) is taken as predictand. Recommended climate indices along with the oceanic and atmospheric data on global scales, for the period April-June are employed as predictors. First 40 years data has been taken as training period and the rest as validation period. Cross-validation stepwise regression approach adopted to select the robust predictors. Upper tropospheric zonal wind at 200 hPa over the northeastern Atlantic is finally selected as the best predictor for interannual model. Besides, the next possible candidate `geopotential height at upper troposphere' is taken as the indirect predictor for being a source of energy transportation from core region (northeast Atlantic/western Europe) to the study area. The model performed well for both the training as well as validation period with correlation coefficient of 0.71 and tolerable root mean square errors. Cross-validation of the model has been processed by incorporating JRA-55 data for potential predictors in addition to NCEP and fragmentation of study period to five non-overlapping test samples. Subsequently, to verify the outcome of the model on physical grounds, observational analyses as well as the model simulations are incorporated. It is revealed that originating from the jet exit region through large vorticity gradients, zonally dominating waves may transport energy and momentum to the downstream areas of west-central Asia, that ultimately affect interannual variability of the specific rainfall. It has been detected that both the circumglobal teleconnection and Rossby wave propagation play vital roles in modulating the proposed mechanism.
Statistical Models and Methods for Network Meta-Analysis.
Madden, L V; Piepho, H-P; Paul, P A
2016-08-01
Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.
Statistical characteristics of irreversible predictability time in regional ocean models
Directory of Open Access Journals (Sweden)
P. C. Chu
2005-01-01
Full Text Available Probabilistic aspects of regional ocean model predictability is analyzed using the probability density function (PDF of the irreversible predictability time (IPT (called τ-PDF computed from an unconstrained ensemble of stochastic perturbations in initial conditions, winds, and open boundary conditions. Two-attractors (a chaotic attractor and a small-amplitude stable limit cycle are found in the wind-driven circulation. Relationship between attractor's residence time and IPT determines the τ-PDF for the short (up to several weeks and intermediate (up to two months predictions. The τ-PDF is usually non-Gaussian but not multi-modal for red-noise perturbations in initial conditions and perturbations in the wind and open boundary conditions. Bifurcation of τ-PDF occurs as the tolerance level varies. Generally, extremely successful predictions (corresponding to the τ-PDF's tail toward large IPT domain are not outliers and share the same statistics as a whole ensemble of predictions.
Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies
McMahon, J; Mahon, John Mc
1995-01-01
An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have sho...
A context dependent pair hidden Markov model for statistical alignment
Arribas-Gil, Ana
2011-01-01
This article proposes a novel approach to statistical alignment of nucleotide sequences by introducing a context dependent structure on the substitution process in the underlying evolutionary model. We propose to estimate alignments and context dependent mutation rates relying on the observation of two homologous sequences. The procedure is based on a generalized pair-hidden Markov structure, where conditional on the alignment path, the nucleotide sequences follow a Markov distribution. We use a stochastic approximation expectation maximization (saem) algorithm to give accurate estimators of parameters and alignments. We provide results both on simulated data and vertebrate genomes, which are known to have a high mutation rate from CG dinucleotide. In particular, we establish that the method improves the accuracy of the alignment of a human pseudogene and its functional gene.
Quantum statistics of Raman scattering model with Stokes mode generation
Tanatar, Bilal; Shumovsky, Alexander S.
1994-01-01
The model describing three coupled quantum oscillators with decay of Rayleigh mode into the Stokes and vibration (phonon) modes is examined. Due to the Manley-Rowe relations the problem of exact eigenvalues and eigenstates is reduced to the calculation of new orthogonal polynomials defined both by the difference and differential equations. The quantum statistical properties are examined in the case when initially: the Stokes mode is in the vacuum state; the Rayleigh mode is in the number state; and the vibration mode is in the number of or squeezed states. The collapses and revivals are obtained for different initial conditions as well as the change in time the sub-Poisson distribution by the super-Poisson distribution and vice versa.
Modelling the influence of photospheric turbulence on solar flare statistics
Mendoza, M.; Kaydul, A.; de Arcangelis, L.; Andrade, J. S., Jr.; Herrmann, H. J.
2014-09-01
Solar flares stem from the reconnection of twisted magnetic field lines in the solar photosphere. The energy and waiting time distributions of these events follow complex patterns that have been carefully considered in the past and that bear some resemblance with earthquakes and stockmarkets. Here we explore in detail the tangling motion of interacting flux tubes anchored in the plasma and the energy ejections resulting when they recombine. The mechanism for energy accumulation and release in the flow is reminiscent of self-organized criticality. From this model, we suggest the origin for two important and widely studied properties of solar flare statistics, including the time-energy correlations. We first propose that the scale-free energy distribution of solar flares is largely due to the twist exerted by the vorticity of the turbulent photosphere. Second, the long-range temporal and time-energy correlations appear to arise from the tube-tube interactions. The agreement with satellite measurements is encouraging.
Statistical model on the surface elevation of waves with breaking
Institute of Scientific and Technical Information of China (English)
2008-01-01
In the surface wind drift layer with constant momentum flux, two sets of the consistent surface eleva- tion expressions with breaking and occurrence conditions for breaking are deduced from the first in- tegrals of the energy and vortex variations and the kinetic and mathematic breaking criterions, then the expression of the surface elevation with wave breaking is established by using the Heaviside function. On the basis of the form of the sea surface elevation with wave breaking and the understanding of small slope sea waves, a triple composite function of real sea waves is presented including the func- tions for the breaking, weak-nonlinear and basic waves. The expression of the triple composite func- tion and the normal distribution of basic waves are the expected theoretical model for surface elevation statistics.
Population stratification using a statistical model on hypergraphs
Vazquez, Alexei
2007-01-01
Population stratification is a problem encountered in several areas of biology and public health. We tackle this problem by mapping a population and its elements attributes into a hypergraph, a natural extension of the concept of graph or network to encode associations among any number of elements. On this hypergraph, we construct a statistical model reflecting our intuition about how the elements attributes can emerge from a postulated population structure. Finally, we introduce the concept of stratification representativeness as a mean to identify the simplest stratification already containing most of the information about the population structure. We demonstrate the power of this framework stratifying an animal and a human population based on phenotypic and genotypic properties, respectively.
Imaging local scatterer concentrations by the Nakagami statistical model.
Tsui, Po-Hsiang; Chang, Chien-Cheng
2007-04-01
The ultrasonic B-mode image is an important clinical tool used to examine the internal structures of the biological tissue. Due to the fact that the conventional B-scans cannot fully reflect the nature of the tissue, some useful quantitative parameters have been applied to quantify the properties of the tissue. Among various possibilities, the Nakagami parameter was demonstrated to have an outstanding ability to detect the variation of the scatterer concentration. This study is aimed to develop a scatterer concentration image based on the Nakagami parameter map to assist in the B-mode image for tissue characterization. In particular, computer simulations are carried out to generate phantoms of different scatterer concentrations and echogenicity coefficients and their B-mode and Nakagami parametric images are compared to evaluate the performance of the Nakagami image in differentiating the properties of the scatterers. The simulated results show that the B-mode image would be affected by the system settings and user operations, whereas the Nakagami parametric image provides a comparatively consistent image result when different diagnosticians use different dynamic ranges and system gains. This is largely because the Nakagami image formation is only based on the backscattered statistics of the ultrasonic signals in local tissues. Such an imaging principle allows the Nakagami image to quantify the local scatterer concentrations in the tissue and to extract the backscattering information from the regions of the weaker echoes that may be lost in the B-mode image. These findings suggest that the Nakagami image can be combined with the use of the B-mode image simultaneously to visualize the tissue structures and the scatterer properties for a better medical diagnosis.
Kerstman, Eric; Minard, Charles; Saile, Lynn; deCarvalho, Mary Freire; Myers, Jerry; Walton, Marlei; Butler, Douglas; Iyengar, Sriram; Johnson-Throop, Kathy; Baumann, David
2009-01-01
The Integrated Medical Model (IMM) is a decision support tool that is useful to mission planners and medical system designers in assessing risks and designing medical systems for space flight missions. The IMM provides an evidence based approach for optimizing medical resources and minimizing risks within space flight operational constraints. The mathematical relationships among mission and crew profiles, medical condition incidence data, in-flight medical resources, potential crew functional impairments, and clinical end-states are established to determine probable mission outcomes. Stochastic computational methods are used to forecast probability distributions of crew health and medical resource utilization, as well as estimates of medical evacuation and loss of crew life. The IMM has been used in support of the International Space Station (ISS) medical kit redesign, the medical component of the ISS Probabilistic Risk Assessment, and the development of the Constellation Medical Conditions List. The IMM also will be used to refine medical requirements for the Constellation program. The IMM outputs for ISS and Constellation design reference missions will be presented to demonstrate the potential of the IMM in assessing risks, planning missions, and designing medical systems. The implementation of the IMM verification and validation plan will be reviewed. Additional planned capabilities of the IMM, including optimization techniques and the inclusion of a mission timeline, will be discussed. Given the space flight constraints of mass, volume, and crew medical training, the IMM is a valuable risk assessment and decision support tool for medical system design and mission planning.
Critical, statistical, and thermodynamical properties of lattice models
Energy Technology Data Exchange (ETDEWEB)
Varma, Vipin Kerala
2013-10-15
In this thesis we investigate zero temperature and low temperature properties - critical, statistical and thermodynamical - of lattice models in the contexts of bosonic cold atom systems, magnetic materials, and non-interacting particles on various lattice geometries. We study quantum phase transitions in the Bose-Hubbard model with higher body interactions, as relevant for optical lattice experiments of strongly interacting bosons, in one and two dimensions; the universality of the Mott insulator to superfluid transition is found to remain unchanged for even large three body interaction strengths. A systematic renormalization procedure is formulated to fully re-sum these higher (three and four) body interactions into the two body terms. In the strongly repulsive limit, we analyse the zero and low temperature physics of interacting hard-core bosons on the kagome lattice at various fillings. Evidence for a disordered phase in the Ising limit of the model is presented; in the strong coupling limit, the transition between the valence bond solid and the superfluid is argued to be first order at the tip of the solid lobe.
System models for PET statistical iterative reconstruction: A review.
Iriarte, A; Marabini, R; Matej, S; Sorzano, C O S; Lewitt, R M
2016-03-01
Positron emission tomography (PET) is a nuclear imaging modality that provides in vivo quantitative measurements of the spatial and temporal distribution of compounds labeled with a positron emitting radionuclide. In the last decades, a tremendous effort has been put into the field of mathematical tomographic image reconstruction algorithms that transform the data registered by a PET camera into an image that represents slices through the scanned object. Iterative image reconstruction methods often provide higher quality images than conventional direct analytical methods. Aside from taking into account the statistical nature of the data, the key advantage of iterative reconstruction techniques is their ability to incorporate detailed models of the data acquisition process. This is mainly realized through the use of the so-called system matrix, that defines the mapping from the object space to the measurement space. The quality of the reconstructed images relies to a great extent on the accuracy with which the system matrix is estimated. Unfortunately, an accurate system matrix is often associated with high reconstruction times and huge storage requirements. Many attempts have been made to achieve realistic models without incurring excessive computational costs. As a result, a wide range of alternatives to the calculation of the system matrix exists. In this article we present a review of the different approaches used to address the problem of how to model, calculate and store the system matrix.
The statistical multifragmentation model: Origins and recent advances
Donangelo, R.; Souza, S. R.
2016-07-01
We review the Statistical Multifragmentation Model (SMM) which considers a generalization of the liquid-drop model for hot nuclei and allows one to calculate thermodynamic quantities characterizing the nuclear ensemble at the disassembly stage. We show how to determine probabilities of definite partitions of finite nuclei and how to determine, through Monte Carlo calculations, observables such as the caloric curve, multiplicity distributions, heat capacity, among others. Some experimental measurements of the caloric curve confirmed the SMM predictions of over 10 years before, leading to a surge in the interest in the model. However, the experimental determination of the fragmentation temperatures relies on the yields of different isotopic species, which were not correctly calculated in the schematic, liquid-drop picture, employed in the SMM. This led to a series of improvements in the SMM, in particular to the more careful choice of nuclear masses and energy densities, specially for the lighter nuclei. With these improvements the SMM is able to make quantitative determinations of isotope production. We show the application of SMM to the production of exotic nuclei through multifragmentation. These preliminary calculations demonstrate the need for a careful choice of the system size and excitation energy to attain maximum yields.
The statistical multifragmentation model: Origins and recent advances
Energy Technology Data Exchange (ETDEWEB)
Donangelo, R., E-mail: donangel@fing.edu.uy [Instituto de Física, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, 11300, Montevideo (Uruguay); Instituto de Física, Universidade Federal do Rio de Janeiro, C.P. 68528, 21941-972 Rio de Janeiro - RJ (Brazil); Souza, S. R., E-mail: srsouza@if.ufrj.br [Instituto de Física, Universidade Federal do Rio de Janeiro, C.P. 68528, 21941-972 Rio de Janeiro - RJ (Brazil); Instituto de Física, Universidade Federal do Rio Grande do Sul, C.P. 15051, 91501-970 Porto Alegre - RS (Brazil)
2016-07-07
We review the Statistical Multifragmentation Model (SMM) which considers a generalization of the liquid-drop model for hot nuclei and allows one to calculate thermodynamic quantities characterizing the nuclear ensemble at the disassembly stage. We show how to determine probabilities of definite partitions of finite nuclei and how to determine, through Monte Carlo calculations, observables such as the caloric curve, multiplicity distributions, heat capacity, among others. Some experimental measurements of the caloric curve confirmed the SMM predictions of over 10 years before, leading to a surge in the interest in the model. However, the experimental determination of the fragmentation temperatures relies on the yields of different isotopic species, which were not correctly calculated in the schematic, liquid-drop picture, employed in the SMM. This led to a series of improvements in the SMM, in particular to the more careful choice of nuclear masses and energy densities, specially for the lighter nuclei. With these improvements the SMM is able to make quantitative determinations of isotope production. We show the application of SMM to the production of exotic nuclei through multifragmentation. These preliminary calculations demonstrate the need for a careful choice of the system size and excitation energy to attain maximum yields.
Terminal-Dependent Statistical Inference for the FBSDEs Models
Directory of Open Access Journals (Sweden)
Yunquan Song
2014-01-01
Full Text Available The original stochastic differential equations (OSDEs and forward-backward stochastic differential equations (FBSDEs are often used to model complex dynamic process that arise in financial, ecological, and many other areas. The main difference between OSDEs and FBSDEs is that the latter is designed to depend on a terminal condition, which is a key factor in some financial and ecological circumstances. It is interesting but challenging to estimate FBSDE parameters from noisy data and the terminal condition. However, to the best of our knowledge, the terminal-dependent statistical inference for such a model has not been explored in the existing literature. We proposed a nonparametric terminal control variables estimation method to address this problem. The reason why we use the terminal control variables is that the newly proposed inference procedures inherit the terminal-dependent characteristic. Through this new proposed method, the estimators of the functional coefficients of the FBSDEs model are obtained. The asymptotic properties of the estimators are also discussed. Simulation studies show that the proposed method gives satisfying estimates for the FBSDE parameters from noisy data and the terminal condition. A simulation is performed to test the feasibility of our method.
Multivariate Statistical Modelling of Drought and Heat Wave Events
Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele
2016-04-01
Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A
Robust model selection and the statistical classification of languages
García, J. E.; González-López, V. A.; Viola, M. L. L.
2012-10-01
In this paper we address the problem of model selection for the set of finite memory stochastic processes with finite alphabet, when the data is contaminated. We consider m independent samples, with more than half of them being realizations of the same stochastic process with law Q, which is the one we want to retrieve. We devise a model selection procedure such that for a sample size large enough, the selected process is the one with law Q. Our model selection strategy is based on estimating relative entropies to select a subset of samples that are realizations of the same law. Although the procedure is valid for any family of finite order Markov models, we will focus on the family of variable length Markov chain models, which include the fixed order Markov chain model family. We define the asymptotic breakdown point (ABDP) for a model selection procedure, and we show the ABDP for our procedure. This means that if the proportion of contaminated samples is smaller than the ABDP, then, as the sample size grows our procedure selects a model for the process with law Q. We also use our procedure in a setting where we have one sample conformed by the concatenation of sub-samples of two or more stochastic processes, with most of the subsamples having law Q. We conducted a simulation study. In the application section we address the question of the statistical classification of languages according to their rhythmic features using speech samples. This is an important open problem in phonology. A persistent difficulty on this problem is that the speech samples correspond to several sentences produced by diverse speakers, corresponding to a mixture of distributions. The usual procedure to deal with this problem has been to choose a subset of the original sample which seems to best represent each language. The selection is made by listening to the samples. In our application we use the full dataset without any preselection of samples. We apply our robust methodology estimating
Directory of Open Access Journals (Sweden)
Thomas Albrecht
2013-01-01
Full Text Available We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models.
Feature and Statistical Model Development in Structural Health Monitoring
Kim, Inho
All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network
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.
Medical Surveillance System & Medical Effect Modeling Thrust Areas
2007-06-01
Equations ( PFE ) developed for this project model physiological systems in biological organisms as 1D liquid or gas flows. Special attention is given...in the model to capturing 2D viscous effects and branching effects. Multiple PFE representations of physiological systems (e.g. the respiratory and
Tanavalee, Chotetawan; Luksanapruksa, Panya; Singhatanadgige, Weerasak
2016-06-01
Microsoft Excel (MS Excel) is a commonly used program for data collection and statistical analysis in biomedical research. However, this program has many limitations, including fewer functions that can be used for analysis and a limited number of total cells compared with dedicated statistical programs. MS Excel cannot complete analyses with blank cells, and cells must be selected manually for analysis. In addition, it requires multiple steps of data transformation and formulas to plot survival analysis graphs, among others. The Megastat add-on program, which will be supported by MS Excel 2016 soon, would eliminate some limitations of using statistic formulas within MS Excel.
Directory of Open Access Journals (Sweden)
J Charan
2014-01-01
Full Text Available Objectives: 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. Design: Systematic review. Materials and Methods: 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." Results: 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. Conclusion: 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.
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.
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
Background 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. Methods 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. Results 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). Conclusion Students' subjective attitudes regarding Cognitive Competence at the beginning of the
Statistical methods in joint modeling of longitudinal and survival data
Dempsey, Walter
Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Ordinarily robust health is associated with longer survival, so the two parts of a survival process cannot be assumed independent. The first part of the thesis is concerned with a general technique---reverse alignment---for constructing statistical models for survival processes. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes. The second part of the thesis explores the concept of a consistent exchangeable survival process---a joint distribution of survival times in which the risk set evolves as a continuous-time Markov process with homogeneous transition rates. A correspondence with the de Finetti approach of constructing an exchangeable survival process by generating iid survival times conditional on a completely independent hazard measure is shown. Several specific processes are detailed, showing how the number of blocks of tied failure times grows asymptotically with the number of individuals in each case. In particular, we show that the set of Markov survival processes with weakly continuous predictive distributions can be characterized by a two-dimensional family called the harmonic process. The outlined methods are then applied to data, showing how they can be easily extended to handle censoring and inhomogeneity among patients.
Xiao, Yong-Zhi; Zhang, Li-Jun; Li, Chun-Mei; Quan, Shi-Yu; Zhou, Min
2011-03-01
The category, number, date of writing, circulated copies and library collection situation of 12275 Japanese medical works before 1912 and 416 Korean medical works before 1910 were analyzed. Most of these medical books are clinical books, which reflected the emphasis on clinical practice and conclusions in the two countries. Japanese medical books were mainly completed between the 17 - 19th century and nearly all of them are conserved as copies, while in Korea the number is 39.6% and we don't know the time that more than half of Korean medical books were completed. The existing books in Japan are collected in more than 500 libraries, especially in 19 public and private libraries., such as Kyoto University Library, Tokyo University Library, Japan Library of Congress. In Korea, most of the books are conserved in Kyujanggak Institute for Korean Studies in Seoul National University. It is necessary for scholars of China, Japan and Korea to work together to study and utilize the ancient medical books of the three countries and to make them play a more important role in the development of Eastern traditional medicine.
A Statistical Framework for the Interpretation of mtDNA Mixtures: Forensic and Medical Applications
Egeland, Thore; Salas, Antonio
2011-01-01
Background Mitochondrial DNA (mtDNA) variation is commonly analyzed in a wide range of different biomedical applications. Cases where more than one individual contribute to a stain genotyped from some biological material give rise to a mixture. Most forensic mixture cases are analyzed using autosomal markers. In rape cases, Y-chromosome markers typically add useful information. However, there are important cases where autosomal and Y-chromosome markers fail to provide useful profiles. In some instances, usually involving small amounts or degraded DNA, mtDNA may be the only useful genetic evidence available. Mitochondrial DNA mixtures also arise in studies dealing with the role of mtDNA variation in tumorigenesis. Such mixtures may be generated by the tumor, but they could also originate in vitro due to inadvertent contamination or a sample mix-up. Methods/Principal Findings We present the statistical methods needed for mixture interpretation and emphasize the modifications required for the more well-known methods based on conventional markers to generalize to mtDNA mixtures. Two scenarios are considered. Firstly, only categorical mtDNA data is assumed available, that is, the variants contributing to the mixture. Secondly, quantitative data (peak heights or areas) on the allelic variants are also accessible. In cases where quantitative information is available in addition to allele designation, it is possible to extract more precise information by using regression models. More precisely, using quantitative information may lead to a unique solution in cases where the qualitative approach points to several possibilities. Importantly, these methods also apply to clinical cases where contamination is a potential alternative explanation for the data. Conclusions/Significance We argue that clinical and forensic scientists should give greater consideration to mtDNA for mixture interpretation. The results and examples show that the analysis of mtDNA mixtures contributes
Kaur, L; Chauhan, R C; Saxena, S C
2005-01-01
This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.
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.
Linear System Models for Ultrasonic Imaging: Application to Signal Statistics
Zemp, Roger J.; Abbey, Craig K.; Insana, Michael F.
2009-01-01
Linear equations for modeling echo signals from shift-variant systems forming ultrasonic B-mode, Doppler, and strain images are analyzed and extended. The approach is based on a solution to the homogeneous wave equation for random inhomogeneous media. When the system is shift-variant, the spatial sensitivity function—defined as a spatial weighting function that determines the scattering volume for a fixed point of time—has advantages over the point-spread function traditionally used to analyze ultrasound systems. Spatial sensitivity functions are necessary for determining statistical moments in the context of rigorous image quality assessment, and they are time-reversed copies of point-spread functions for shift variant systems. A criterion is proposed to assess the validity of a local shift-invariance assumption. The analysis reveals realistic situations in which in-phase signals are correlated to the corresponding quadrature signals, which has strong implications for assessing lesion detectability. Also revealed is an opportunity to enhance near- and far-field spatial resolution by matched filtering unfocused beams. The analysis connects several well-known approaches to modeling ultrasonic echo signals. PMID:12839176
Detection of hydrogeochemical seismic precursors by a statistical learning model
Directory of Open Access Journals (Sweden)
L. Castellana
2008-11-01
Full Text Available The problem of detecting the occurrence of an earthquake precursor is faced in the general framework of the statistical learning theory. The aim of this work is both to build models able to detect seismic precursors from time series of different geochemical signals and to provide an estimate of number of false positives. The model we used is k-Nearest-Neighbor classifier for discriminating "no-disturbed signal", "seismic precursor" and "co-post seismic precursor" in time series relative to thirteen different hydrogeochemical parameters collected in water samples from a natural spring in Kamchachta (Russia peninsula. The measurements collected are ion content (Na, Cl, Ca, HCO_{3}, H_{3}BO_{3}, parameters (pH, Q, T and gases (N_{2}, CO_{2}, CH_{4}, O_{2}, Ag. The classification error is measured by Leave-K-Out-Cross-Validation procedure. Our study shows that the most discriminative ions for detecting seismic precursors are Cl and Na having an error rates of 15%. Moreover, the most discriminative parameters and gases are Q and CH_{4} respectively, with error rate of 21%. The ions result the most informative hydrogeochemicals for detecting seismic precursors due to the peculiarities of the mechanisms involved in earthquake preparation. Finally we show that the information collected some month before the event under analysis are necessary to improve the classification accuracy.
PROBABILISTIC-STATISTICAL MODELING THE INTERFERENCES FROM ELECTRIC LOCOMOTIVES
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-02-01
Full Text Available The movements of electric locomotives create the interferences affecting the wired link. The creation of sufficiently technical effective and at the same time cost-effective means of protection from wireline interferences generated traction networks assumes as a preparatory phase to develop mathematical models of interference caused by electric locomotives. We have developed a probabilistic-statistical model of interferences caused by electric locomotives. The asymptotic distribution of the total interference is the distribution of the length of the two-dimensional random vector whose coordinates - independent normally distributed random variables with mean 0 and variance 1. Limit theorem is proved for the expectation of the total amplitude of the interferences. Monte-Carlo method is used to study the rate of convergence of the expectation of the total amplitude of the interferences to the limiting value. We used an algorithm of mixing developed by MacLaren-Marsaglia (M-algorithm. Five sets of amplitudes are analyzed, selected in accordance with the recommendations of experts in the field of traction AC networks. The most rapid convergence to the limit takes place in the case of equal amplitudes. It was found that the maximum possible average value of the amplitude of the random noise by 7.4% less than the previously used value, which promises a significant economic impact
Mathematical-statistical models of generated hazardous hospital solid waste.
Awad, A R; Obeidat, M; Al-Shareef, M
2004-01-01
This research work was carried out under the assumption that wastes generated from hospitals in Irbid, Jordan were hazardous. The hazardous and non-hazardous wastes generated from the different divisions in the three hospitals under consideration were not separated during collection process. Three hospitals, Princess Basma hospital (public), Princess Bade'ah hospital (teaching), and Ibn Al-Nafis hospital (private) in Irbid were selected for this study. The research work took into account the amounts of solid waste accumulated from each division and also determined the total amount generated from each hospital. The generation rates were determined (kilogram per patient, per day; kilogram per bed, per day) for the three hospitals. These generation rates were compared with similar hospitals in Europe. The evaluation suggested that the current situation regarding the management of these wastes in the three studied hospitals needs revision as these hospitals do not follow methods of waste disposals that would reduce risk to human health and the environment practiced in developed countries. Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching, private). In these models number of patients, beds, and type of hospital were revealed to be significant factors on quantity of waste generated. Multiple regressions were also used to estimate the quantities of wastes generated from similar divisions in the three hospitals (surgery, internal diseases, and maternity).
Algebraic Statistical Model for Biochemical Network Dynamics Inference.
Linder, Daniel F; Rempala, Grzegorz A
2013-12-01
With modern molecular quantification methods, like, for instance, high throughput sequencing, biologists may perform multiple complex experiments and collect longitudinal data on RNA and DNA concentrations. Such data may be then used to infer cellular level interactions between the molecular entities of interest. One method which formalizes such inference is the stoichiometric algebraic statistical model (SASM) of [2] which allows to analyze the so-called conic (or single source) networks. Despite its intuitive appeal, up until now the SASM has been only heuristically studied on few simple examples. The current paper provides a more formal mathematical treatment of the SASM, expanding the original model to a wider class of reaction systems decomposable into multiple conic subnetworks. In particular, it is proved here that on such networks the SASM enjoys the so-called sparsistency property, that is, it asymptotically (with the number of observed network trajectories) discards the false interactions by setting their reaction rates to zero. For illustration, we apply the extended SASM to in silico data from a generic decomposable network as well as to biological data from an experimental search for a possible transcription factor for the heat shock protein 70 (Hsp70) in the zebrafish retina.
Medical models and metaphors for depression.
Patten, S B
2015-08-01
The aetiology of depression is not fully understood, which allows many different perspectives on aetiology to be adopted. Researchers and clinicians may be attracted to concepts of aetiology that parallel other diagnoses with which they are familiar. Such parallels may assume the role of informal models or metaphors for depressive disorders. They may even function as informal scientific theories of aetiology, energising research activities by guiding hypothesis generation and organising new knowledge. Parallels between different types of disease may ultimately prove valuable as frameworks supporting the emergence and maturation of new knowledge. However, such models may be counterproductive if their basis, which is likely to lay at least partially in analogy, is unacknowledged or overlooked. This could cause such models to appear more compelling than they really are. Listing examples of situations in which models of depression may arise from, or be strengthened by, parallels to other familiar conditions may increase the accessibility of such models either to criticism or support. However, such a list has not yet appeared in the literature. The present paper was written with the modest goal of stating several examples of models or metaphors for depression. This paper adopted narrative review methods. The intention was not to produce a comprehensive list of such ideas, but rather to identify prominent examples of ways of thinking about depression that may have been invigorated as a result parallels with other types of disease. Eight possible models are identified: depressive disorders as chemical imbalances (e.g., a presumed or theoretical imbalance of normally balanced neurotransmission in the brain), degenerative conditions (e.g., a brain disease characterised by atrophy of specified brain structures), toxicological syndromes (a result of exposure to a noxious psychological environment), injuries (e.g., externally induced brain damage related to stress), deficiency
WE-A-201-00: Anne and Donald Herbert Distinguished Lectureship On Modern Statistical Modeling.
Deasy, Joseph
2016-06-01
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 Regulatory
2014-12-01
suggestions for reducing this burden to Washington Headquarters Service , Directorate for Information Operations and Reports 1215 Jefferson Davis...caveat in the population statistics shown in Figure 10 is that the data represent aggregations of both juveniles and adults, nighttime and daytime...to left. In this plot, black circles indicate the raw position data; the blue line represents a corrected path after fdtering’to remove frame rate
Ice Shelf Modeling: A Cross-Polar Bayesian Statistical Approach
Kirchner, N.; Furrer, R.; Jakobsson, M.; Zwally, H. J.
2010-12-01
Ice streams interlink glacial terrestrial and marine environments: embedded in a grounded inland ice such as the Antarctic Ice Sheet or the paleo ice sheets covering extensive parts of the Eurasian and Amerasian Arctic respectively, ice streams are major drainage agents facilitating the discharge of substantial portions of continental ice into the ocean. At their seaward side, ice streams can either extend onto the ocean as floating ice tongues (such as the Drygalsky Ice Tongue/East Antarctica), or feed large ice shelves (as is the case for e.g. the Siple Coast and the Ross Ice Shelf/West Antarctica). The flow behavior of ice streams has been recognized to be intimately linked with configurational changes in their attached ice shelves; in particular, ice shelf disintegration is associated with rapid ice stream retreat and increased mass discharge from the continental ice mass, contributing eventually to sea level rise. Investigations of ice stream retreat mechanism are however incomplete if based on terrestrial records only: rather, the dynamics of ice shelves (and, eventually, the impact of the ocean on the latter) must be accounted for. However, since floating ice shelves leave hardly any traces behind when melting, uncertainty regarding the spatio-temporal distribution and evolution of ice shelves in times prior to instrumented and recorded observation is high, calling thus for a statistical modeling approach. Complementing ongoing large-scale numerical modeling efforts (Pollard & DeConto, 2009), we model the configuration of ice shelves by using a Bayesian Hiearchial Modeling (BHM) approach. We adopt a cross-polar perspective accounting for the fact that currently, ice shelves exist mainly along the coastline of Antarctica (and are virtually non-existing in the Arctic), while Arctic Ocean ice shelves repeatedly impacted the Arctic ocean basin during former glacial periods. Modeled Arctic ocean ice shelf configurations are compared with geological spatial
Indian Academy of Sciences (India)
Yin Y. Shugart; Bing-Jian Feng; Andrew Collins
2002-11-01
We have evaluated the power for detecting a common trait determined by two loci, using seven statistics, of which five are implemented in the computer program SimWalk2, and two are implemented in GENEHUNTER. Unlike most previous reports which involve evaluations of the power of allele-sharing statistics for a single disease locus, we have used a simulated data set of general pedigrees in which a two-locus disease is segregating and evaluated several non-parametric linkage statistics implemented in the two programs. We found that the power for detecting linkage using the $S_{\\text{all}}$ statistic in GENEHUNTER (GH, version 2.1), implemented as statistic in SimWalk2 (version 2.82), is different in the two. The values associated with statistic output by SimWalk2 are consistently more conservative than those from GENEHUNTER except when the underlying model includes heterogeneity at a level of 50% where the values output are very comparable. On the other hand, when the thresholds are determined empirically under the null hypothesis, $S_{\\text{all}}$ in GENEHUNTER and statistic have similar power.
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......, 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....
A statistical model of a metallic inclusion in semiconducting media
Shikin, V. B.
2016-11-01
The properties of an isolated multicharged atom embedded into a semiconducting medium are discussed. The analysis generalizes the results of the known Thomas-Fermi theory for a multicharged ( Z ≫ 1) atom in vacuum when it is immersed into an electron-hole gas of finite temperature. The Thomas-Fermi-Debye (TFD) atom problem is directly related to the properties of donors in low-doped semiconductors and is alternative in its conclusions to the ideal scenario of dissociation of donors. In the existing ideal statistics, an individual donor under infinitely low doping is completely ionized (a charged center does not hold its neutralizing counter-ions). A Thomas-Fermi-Debye atom (briefly, a TFD donor) remains a neutral formation that holds its screening "coat" even for infinitely low doping level, i.e., in the region of n d λ0 3 ≪ 1, where n d is the concentration of the doping impurity and λ0 is the Debye length with the parameters of intrinsic semiconductor. Various observed consequences in the behavior of a TFD donor are discussed that allow one to judge the reality of the implications of the TFD donor model.
Increased Statistical Efficiency in a Lognormal Mean Model
Directory of Open Access Journals (Sweden)
Grant H. Skrepnek
2014-01-01
Full Text Available Within the context of clinical and other scientific research, a substantial need exists for an accurate determination of the point estimate in a lognormal mean model, given that highly skewed data are often present. As such, logarithmic transformations are often advocated to achieve the assumptions of parametric statistical inference. Despite this, existing approaches that utilize only a sample’s mean and variance may not necessarily yield the most efficient estimator. The current investigation developed and tested an improved efficient point estimator for a lognormal mean by capturing more complete information via the sample’s coefficient of variation. Results of an empirical simulation study across varying sample sizes and population standard deviations indicated relative improvements in efficiency of up to 129.47 percent compared to the usual maximum likelihood estimator and up to 21.33 absolute percentage points above the efficient estimator presented by Shen and colleagues (2006. The relative efficiency of the proposed estimator increased particularly as a function of decreasing sample size and increasing population standard deviation.
Cerrolaza, Juan J; Villanueva, Arantxa; Cabeza, Rafael
2012-03-01
The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.
Olive mill wastewater characteristics: modelling and statistical analysis
Directory of Open Access Journals (Sweden)
Martins-Dias, Susete
2004-09-01
Full Text Available A synthesis of the work carried out on Olive Mill Wastewater (OMW characterisation is given, covering articles published over the last 50 years. Data on OMW characterisation found in the literature are summarised and correlations between them and with phenolic compounds content are sought. This permits the characteristics of an OMW to be estimated from one simple measurement: the phenolic compounds concentration. A model based on OMW characterisations accounting 6 countries was developed along with a model for Portuguese OMW. The statistical analysis of the correlations obtained indicates that Chemical Oxygen Demand of a given OMW is a second-degree polynomial function of its phenolic compounds concentration. Tests to evaluate the regressions significance were carried out, based on multivariable ANOVA analysis, on visual standardised residuals distribution and their means for confidence levels of 95 and 99 %, validating clearly these models. This modelling work will help in the future planning, operation and monitoring of an OMW treatment plant.Presentamos una síntesis de los trabajos realizados en los últimos 50 años relacionados con la caracterización del alpechín. Realizamos una recopilación de los datos publicados, buscando correlaciones entre los datos relativos al alpechín y los compuestos fenólicos. Esto permite la determinación de las características del alpechín a partir de una sola medida: La concentración de compuestos fenólicos. Proponemos dos modelos, uno basado en datos relativos a seis países y un segundo aplicado únicamente a Portugal. El análisis estadístico de las correlaciones obtenidas indica que la demanda química de oxígeno de un determinado alpechín es una función polinómica de segundo grado de su concentración de compuestos fenólicos. Se comprobó la significancia de esta correlación mediante la aplicación del análisis multivariable ANOVA, y además se evaluó la distribución de residuos y sus
Statistical Damage Detection of Civil Engineering Structures using ARMAV Models
DEFF Research Database (Denmark)
Andersen, P.; Kirkegaard, Poul Henning
In this paper a statistically based damage detection of a lattice steel mast is performed. By estimation of the modal parameters and their uncertainties it is possible to detect whether some of the modal parameters have changed with a statistical significance. The estimation of the uncertainties ...
Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration.
Ehrhardt, Jan; Werner, René; Schmidt-Richberg, Alexander; Handels, Heinz
2011-02-01
Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory
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.
A New Statistic for Evaluating Item Response Theory Models for Ordinal Data. CRESST Report 839
Cai, Li; Monroe, Scott
2014-01-01
We propose a new limited-information goodness of fit test statistic C[subscript 2] for ordinal IRT models. The construction of the new statistic lies formally between the M[subscript 2] statistic of Maydeu-Olivares and Joe (2006), which utilizes first and second order marginal probabilities, and the M*[subscript 2] statistic of Cai and Hansen…
DEFF Research Database (Denmark)
Wang, X.; Heimann, T.; Lo, P.
2012-01-01
The segmentation of tree-like tubular structures such as coronary arteries and airways is an essential step for many 3D medical imaging applications. Statistical tracking techniques for the extraction of elongated structures have received considerable attention in recent years due to their robust......The segmentation of tree-like tubular structures such as coronary arteries and airways is an essential step for many 3D medical imaging applications. Statistical tracking techniques for the extraction of elongated structures have received considerable attention in recent years due...... to their robustness against image noise and pathological changes. However, most tracking methods are limited to a specific application and do not support branching structures efficiently. In this work, we present a novel statistical tracking approach for the extraction of different types of tubular structures...... sets and distal airways from 20 CT scans. These data sets were provided by the organizers of the workshop '3D Segmentation in the Clinic: A Grand Challenge II-Coronary Artery Tracking (CAT08)' and 'Extraction of Airways from CT 2009 (EXACT'09)'. On average, 81.5% overlap and 0.51 mm accuracy...
Inference Based on Simple Step Statistics for the Location Model.
1981-07-01
function. Let TN,k(9) - Zak(’)Vi(e). Then TNk is called the k-step statistic. Noether (1973) studied the 1-step statistic with particular emphasis on...opposed to the sign statistic. These latter two comparisons were first discussed by Noether (1973) in a somewhat different setting. Notice that the...obtained by Noether (1973). If k - 3, we seek the (C + 1)’st and (2N - bI - b2 - C)’th ordered Walsh averages in D The algorithm of Section 3 modified to
Fisher-Schroedinger models for statistical encryption of covert information
Venkatesan, R. C.
2007-04-01
The theoretical framework for a principled procedure to encrypt/decrypt covert information (code)into/from the null spaces of a hierarchy of statistical distributions possessing ill-conditioned eigenstructures, is suggested. The statistical distributions are inferred using incomplete constraints, employing the generalized nonextensive thermostatistics (NET) Fisher information as the measure of uncertainty. The hierarchy of inferred statistical distributions possess a quantum mechanical connotation for unit values of the nonextensivity parameter. A systematic strategy to encrypt/decrypt code via unitary projections into the null spaces of the ill-conditioned eigenstructures, is presented.
CONFIDENCE REGIONS IN TERMS OF STATISTICAL CURVATURE FOR AR(q) NONLINEAR REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
刘应安; 韦博成
2004-01-01
This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence regions for parameters and parameter subsets in terms of statistical curvatures are given based on the likelihood ratio statistics and score statistics. Several previous results, such as [1] and [2] are extended to AR(q)nonlinear regression models.
Modelling malaria treatment practices in Bangladesh using spatial statistics
Directory of Open Access Journals (Sweden)
Haque Ubydul
2012-03-01
Full Text Available Abstract Background Malaria treatment-seeking practices vary worldwide and Bangladesh is no exception. Individuals from 88 villages in Rajasthali were asked about their treatment-seeking practices. A portion of these households preferred malaria treatment from the National Control Programme, but still a large number of households continued to use drug vendors and approximately one fourth of the individuals surveyed relied exclusively on non-control programme treatments. The risks of low-control programme usage include incomplete malaria treatment, possible misuse of anti-malarial drugs, and an increased potential for drug resistance. Methods The spatial patterns of treatment-seeking practices were first examined using hot-spot analysis (Local Getis-Ord Gi statistic and then modelled using regression. Ordinary least squares (OLS regression identified key factors explaining more than 80% of the variation in control programme and vendor treatment preferences. Geographically weighted regression (GWR was then used to assess where each factor was a strong predictor of treatment-seeking preferences. Results Several factors including tribal affiliation, housing materials, household densities, education levels, and proximity to the regional urban centre, were found to be effective predictors of malaria treatment-seeking preferences. The predictive strength of each of these factors, however, varied across the study area. While education, for example, was a strong predictor in some villages, it was less important for predicting treatment-seeking outcomes in other villages. Conclusion Understanding where each factor is a strong predictor of treatment-seeking outcomes may help in planning targeted interventions aimed at increasing control programme usage. Suggested strategies include providing additional training for the Building Resources across Communities (BRAC health workers, implementing educational programmes, and addressing economic factors.
Statistical Design, Models and Analysis for the Job Change Framework.
Gleser, Leon Jay
1990-01-01
Proposes statistical methodology for testing Loughead and Black's "job change thermostat." Discusses choice of target population; relationship between job satisfaction and values, perceptions, and opportunities; and determinants of job change. (SK)
Monthly to seasonal low flow prediction: statistical versus dynamical models
Ionita-Scholz, Monica; Klein, Bastian; Meissner, Dennis; Rademacher, Silke
2016-04-01
the Alfred Wegener Institute a purely statistical scheme to generate streamflow forecasts for several months ahead. Instead of directly using teleconnection indices (e.g. NAO, AO) the idea is to identify regions with stable teleconnections between different global climate information (e.g. sea surface temperature, geopotential height etc.) and streamflow at different gauges relevant for inland waterway transport. So-called stability (correlation) maps are generated showing regions where streamflow and climate variable from previous months are significantly correlated in a 21 (31) years moving window. Finally, the optimal forecast model is established based on a multiple regression analysis of the stable predictors. We will present current results of the aforementioned approaches with focus on the River Rhine (being one of the world's most frequented waterways and the backbone of the European inland waterway network) and the Elbe River. Overall, our analysis reveals the existence of a valuable predictability of the low flows at monthly and seasonal time scales, a result that may be useful to water resources management. Given that all predictors used in the models are available at the end of each month, the forecast scheme can be used operationally to predict extreme events and to provide early warnings for upcoming low flows.
Statistical models for estimating daily streamflow in Michigan
Holtschlag, D.J.; Salehi, Habib
1992-01-01
Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan. Stations were paired by randomly choosing a station operated in 1989 at which 10 or more years of continuous flow data had been collected and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are similar. Streamflow data from the 25 randomly selected stations were used as the response variables; streamflow data at the nearby stations were used to generate a set of explanatory variables. Ordinary-least squares regression (OLSR) equations, autoregressive integrated moving-average (ARIMA) equations, and transfer function-noise (TFN) equations were developed to estimate the log transform of flow for the 25 randomly selected stations. The precision of each type of equation was evaluated on the basis of the standard deviation of the estimation errors. OLSR equations produce one set of estimation errors; ARIMA and TFN models each produce l sets of estimation errors corresponding to the forecast lead. The lead-l forecast is the estimate of flow l days ahead of the most recent streamflow used as a response variable in the estimation. In this analysis, the standard deviation of lead l ARIMA and TFN forecast errors were generally lower than the standard deviation of OLSR errors for l weighted average of forecasts based on TFN equations and backcasts (forecasts of the reverse-ordered series) based on ARIMA equations. The standard deviation of composite errors varied throughout the length of the estimation interval and generally was at maximum near the center of the interval. For comparison with OLSR errors, the mean standard deviation of composite errors were computed for intervals of length 1 to 40 days. The mean standard deviation of length-l composite errors were generally less than the standard deviation of the OLSR errors for l error magnitudes were compared by computing ratios of the mean standard deviation
Preventable Medical Errors Driven Modeling of Medical Best Practice Guidance Systems.
Ou, Andrew Y-Z; Jiang, Yu; Wu, Po-Liang; Sha, Lui; Berlin, Richard B
2017-01-01
In a medical environment such as Intensive Care Unit, there are many possible reasons to cause errors, and one important reason is the effect of human intellectual tasks. When designing an interactive healthcare system such as medical Cyber-Physical-Human Systems (CPHSystems), it is important to consider whether the system design can mitigate the errors caused by these tasks or not. In this paper, we first introduce five categories of generic intellectual tasks of humans, where tasks among each category may lead to potential medical errors. Then, we present an integrated modeling framework to model a medical CPHSystem and use UPPAAL as the foundation to integrate and verify the whole medical CPHSystem design models. With a verified and comprehensive model capturing the human intellectual tasks effects, we can design a more accurate and acceptable system. We use a cardiac arrest resuscitation guidance and navigation system (CAR-GNSystem) for such medical CPHSystem modeling. Experimental results show that the CPHSystem models help determine system design flaws and can mitigate the potential medical errors caused by the human intellectual tasks.
Statistical Inference Models for Image Datasets with Systematic Variations.
Kim, Won Hwa; Bendlin, Barbara B; Chung, Moo K; Johnson, Sterling C; Singh, Vikas
2015-06-01
Statistical analysis of longitudinal or cross sectional brain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience. However, when there are systematic variations in the images due to parameter changes such as changes in the scanner protocol, hardware changes, or when combining data from multi-site studies, the statistical analysis becomes problematic. Motivated by this scenario, the goal of this paper is to develop a unified statistical solution to the problem of systematic variations in statistical image analysis. Based in part on recent literature in harmonic analysis on diffusion maps, we propose an algorithm which compares operators that are resilient to the systematic variations. These operators are derived from the empirical measurements of the image data and provide an efficient surrogate to capturing the actual changes across images. We also establish a connection between our method to the design of wavelets in non-Euclidean space. To evaluate the proposed ideas, we present various experimental results on detecting changes in simulations as well as show how the method offers improved statistical power in the analysis of real longitudinal PIB-PET imaging data acquired from participants at risk for Alzheimer's disease (AD).
Statistical Inference Models for Image Datasets with Systematic Variations
Kim, Won Hwa; Bendlin, Barbara B.; Chung, Moo K.; Johnson, Sterling C.; Singh, Vikas
2016-01-01
Statistical analysis of longitudinal or cross sectional brain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience. However, when there are systematic variations in the images due to parameter changes such as changes in the scanner protocol, hardware changes, or when combining data from multi-site studies, the statistical analysis becomes problematic. Motivated by this scenario, the goal of this paper is to develop a unified statistical solution to the problem of systematic variations in statistical image analysis. Based in part on recent literature in harmonic analysis on diffusion maps, we propose an algorithm which compares operators that are resilient to the systematic variations. These operators are derived from the empirical measurements of the image data and provide an efficient surrogate to capturing the actual changes across images. We also establish a connection between our method to the design of wavelets in non-Euclidean space. To evaluate the proposed ideas, we present various experimental results on detecting changes in simulations as well as show how the method offers improved statistical power in the analysis of real longitudinal PIB-PET imaging data acquired from participants at risk for Alzheimer’s disease (AD). PMID:26989336
Directory of Open Access Journals (Sweden)
Kelly M Goedert
2013-05-01
Full Text Available Valid research on neglect rehabilitation demands a statistical approach commensurate with the characteristics of neglect rehabilitation data: Neglect arises from impairment in distinct brain networks leading to large between-subject variability in baseline symptoms and recovery trajectories. Studies enrolling medically-ill, disabled patients, may suffer from missing, unbalanced data, and small sample sizes. Finally, assessment of rehabilitation requires a description of continuous recovery trajectories. Unfortunately, the statistical method currently employed in most studies of neglect treatment (repeated-measures ANOVA does not well-address these issues. Here we review an alternative, mixed linear modeling (MLM, that is more appropriate for assessing change over time. MLM better accounts for between-subject heterogeneity in baseline neglect severity and in recovery trajectory. MLM does not require complete or balanced data, nor does it make strict assumptions regarding the data structure. Furthermore, because MLM better models between-subject heterogeneity it often results in increased power to observe treatment effects with smaller samples. After reviewing current practices in the field, and the assumptions of repeated-measures ANOVA, we provide an introduction to MLM. We review its assumptions, uses, advantages and disadvantages. Using real and simulated data, we illustrate how MLM may improve the ability to detect effects of treatment over ANOVA, particularly with the small samples typical of neglect research. Furthermore, our simulation analyses result in recommendations for the design of future rehabilitation studies. Because between-subject heterogeneity is one important reason why studies of neglect treatments often yield conflicting results, employing statistical procedures that model this heterogeneity more accurately will increase the efficiency of our efforts to find treatments to improve the lives of individuals with neglect.
Information Geometric Complexity of a Trivariate Gaussian Statistical Model
Directory of Open Access Journals (Sweden)
Domenico Felice
2014-05-01
Full Text Available We evaluate the information geometric complexity of entropic motion on low-dimensional Gaussian statistical manifolds in order to quantify how difficult it is to make macroscopic predictions about systems in the presence of limited information. Specifically, we observe that the complexity of such entropic inferences not only depends on the amount of available pieces of information but also on the manner in which such pieces are correlated. Finally, we uncover that, for certain correlational structures, the impossibility of reaching the most favorable configuration from an entropic inference viewpoint seems to lead to an information geometric analog of the well-known frustration effect that occurs in statistical physics.
A robust physics-oriented statistical GaAs MESFET model
Bandler, J.W.; Biernacki, R.M.; Cai, Q; Chen, S. H.
1994-01-01
In this paper we present a robust physics-oriented statistical GaAs MESFET model. Our model integrates the DC Khatibzadeh and Trew model for DC simulation with the Ladbrooke formulas for small-signal analysis (KTL). Accuracy of the statistical KTL model is verified by Monte Carlo simulations using device measurements. Statistical extraction and postprocessing of device physical parameters are carried out by HarPE.
Evaluation of Medical Cystine Stone Prevention in an Animal Model
Sagi, Sreedhar; Wendt-Nordahl, Gunnar; Alken, Peter; Knoll, Thomas
2007-04-01
Medical treatment for cystinuria aims to decrease the concentration of cystine in the urine, increase its solubility and therefore prevent stone formation. Ascorbic acid and captopril have been recommended as alternatives to thiol drugs, though conflicting data undermining their efficacy has been widely reported, too. The aim of this study was to verify the effects of ascorbic acid and captopril on cystine stone formation in the cystinuria mouse model. A total of 28 male homozygous pebbles mice were used for characterizing the mice on normal diet, ascorbic acid and captopril supplemented diets. The baseline physiological parameters of the mice were determined initially. The normal diet was then replaced with the supplemented diet (ascorbic acid/captopril) for the next 48 weeks and various biochemical parameters in urine and plasma were analyzed. All homozygous mice developed urinary cystine stones during the first year of life. No reduction in the urinary cystine concentration was seen with either of the supplemented diets. The stone mass varied widely in the study and a beneficial effect of ascorbic acid in some of the animals was possible though an overall statistical significance was not seen. Conclusions: The cystinuria mouse model provides an ideal tool for evaluation of stone preventive measures in a standardized environment. This study confirms that ascorbic acid and captopril are not effective in cystinuria.
Modeling Attitude toward Statistics by a Structural Equation
Escalera-Chávez, Milka Elena; García-Santillán, Arturo; Venegas-Martínez, Francisco
2014-01-01
In this study, we examined whether the constructs of usefulness, motivation, likeness, confidence, and anxiety influence the student's attitude towards statistics. Two hundred ninety eight students enrolled in the private university were surveyed by using the questionnaire proposed by Auzmendi (1992). Data analysis was done by structural…
Monte Carlo simulation of quantum statistical lattice models
Raedt, Hans De; Lagendijk, Ad
1985-01-01
In this article we review recent developments in computational methods for quantum statistical lattice problems. We begin by giving the necessary mathematical basis, the generalized Trotter formula, and discuss the computational tools, exact summations and Monte Carlo simulation, that will be used t
Assessing Statistical Aspects of Test Fairness with Structural Equation Modelling
Kline, Rex B.
2013-01-01
Test fairness and test bias are not synonymous concepts. Test bias refers to statistical evidence that the psychometrics or interpretation of test scores depend on group membership, such as gender or race, when such differences are not expected. A test that is grossly biased may be judged to be unfair, but test fairness concerns the broader, more…