Multivariate Discrete First Order Stochastic Dominance

DEFF Research Database (Denmark)

Tarp, Finn; Østerdal, Lars Peter

This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution  f first order stochastic dominates distribution g if and only if  f can be obtained from g by iteratively shifting density from one outcome to another...

Clinical outcomes after final kissing balloon inflation compared with no final kissing balloon inflation in bifurcation lesions treated with a dedicated coronary bifurcation stent

NARCIS (Netherlands)

Grundeken, Maik J.; Lesiak, Maciej; Asgedom, Solomon; Garcia, Eulogio; Bethencourt, Armando; Norell, Michael S.; Damman, Peter; Woudstra, Pier; Koch, Karel T.; Vis, M. Marije; Henriques, Jose P.; Tijssen, Jan G.; Onuma, Yoshinobu; Foley, David P.; Bartorelli, Antonio L.; Stella, Pieter R.; de Winter, Robbert J.; Wykrzykowska, Joanna J.

2014-01-01

We evaluated differences in clinical outcomes between patients who underwent final kissing balloon inflation (FKBI) and patients who did not undergo FKBI in bifurcation treatment using the Tryton Side Branch Stent (Tryton Medical, Durham, North Carolina, USA). Clinical outcomes were defined as

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes.

Science.gov (United States)

Achana, Felix A; Cooper, Nicola J; Bujkiewicz, Sylwia; Hubbard, Stephanie J; Kendrick, Denise; Jones, David R; Sutton, Alex J

2014-07-21

Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on

Predictive factors for final outcome of severely traumatized eyes with no light perception

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Agrawal Rupesh

2012-06-01

Full Text Available Abstract Background An eye injury that causes no light perception (NLP typically carries an unfavorable prognosis, and NLP because of trauma is a common indication for enucleation. With advances in vitreoretinal surgical techniques, however, the indication for enucleation is no longer determined by posttrauma NLP vision alone. There are limited studies in the literature to analyse the outcome of NLP eyes following open globe injury. The current study was aimed to evaluate the outcome of surgical repair of severely traumatized eyes with no light perception vision as preoperative visual acuity. Secondary objective was to possibly predict the factors affecting the final vision outcome in this eyes. Methods Retrospective case analysis of patients with surgical repair of open globe injury over last ten years at a tertiary referral eye care centre in Singapore. Results Out of one hundred and seventy two eyes with open globe injury 27 (15.7% eyes had no light perception (NLP. After surgical repair, final visual acuity remained NLP in 18 (66.7% eyes. Final vision improved to Light perception/ Hand movement (LP/HM in 2(7.4% eyes, 1/200 to 19/200(11.1% in 3 eyes and 20/50-20/200(14.8% in 4 eyes. The median follow up was 18.9 months (range: 4–60 months. The factors contributing to poor postoperative outcome were presence of RAPD (p = 0.014, wound extending into zone III (p = 0.023 and associated vitreoretinal trauma (p = 0.008. Conclusions One third of eyes had ambulatory vision or better though two third of eyes still remained NLP. Pre-operative visual acuity of NLP should not be an indication for primary enucleation or evisceration for severely traumatized eyes. Presence of afferent papillary defect, wound extending posterior to rectus insertion and associated vitreoretinal trauma can adversely affect the outcome in severely traumatized eyes with NLP. Timely intervention and state of art surgery may restore useful vision in severely

Agent-Based Decision Control—How to Appreciate Multivariate Optimisation in Architecture

DEFF Research Database (Denmark)

Negendahl, Kristoffer; Perkov, Thomas Holmer; Kolarik, Jakub

2015-01-01

, the method is applied to a multivariate optimisation problem. The aim is specifically to demonstrate optimisation for entire building energy consumption, daylight distribution and capital cost. Based on the demonstrations Moth’s ability to find local minima is discussed. It is concluded that agent-based...... in the early design stage. The main focus is to demonstrate the optimisation method, which is done in two ways. Firstly, the newly developed agent-based optimisation algorithm named Moth is tested on three different single objective search spaces. Here Moth is compared to two evolutionary algorithms. Secondly...... optimisation algorithms like Moth open up for new uses of optimisation in the early design stage. With Moth the final outcome is less dependent on pre- and post-processing, and Moth allows user intervention during optimisation. Therefore, agent-based models for optimisation such as Moth can be a powerful...

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

Science.gov (United States)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Prediction of preterm birth in multiple pregnancies: development of a multivariable model including cervical length measurement at 16 to 21 weeks' gestation.

Science.gov (United States)

van de Mheen, Lidewij; Schuit, Ewoud; Lim, Arianne C; Porath, Martina M; Papatsonis, Dimitri; Erwich, Jan J; van Eyck, Jim; van Oirschot, Charlotte M; Hummel, Piet; Duvekot, Johannes J; Hasaart, Tom H M; Groenwold, Rolf H H; Moons, Karl G M; de Groot, Christianne J M; Bruinse, Hein W; van Pampus, Maria G; Mol, Ben W J

2014-04-01

To develop a multivariable prognostic model for the risk of preterm delivery in women with multiple pregnancy that includes cervical length measurement at 16 to 21 weeks' gestation and other variables. We used data from a previous randomized trial. We assessed the association between maternal and pregnancy characteristics including cervical length measurement at 16 to 21 weeks' gestation and time to delivery using multivariable Cox regression modelling. Performance of the final model was assessed for the outcomes of preterm and very preterm delivery using calibration and discrimination measures. We studied 507 women, of whom 270 (53%) delivered models for preterm and very preterm delivery had a c-index of 0.68 (95% CI 0.63 to 0.72) and 0.68 (95% CI 0.62 to 0.75), respectively, and showed good calibration. In women with a multiple pregnancy, the risk of preterm delivery can be assessed with a multivariable model incorporating cervical length and other predictors.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

Science.gov (United States)

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Data classification and MTBF prediction with a multivariate analysis approach

International Nuclear Information System (INIS)

Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco

2012-01-01

The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

Science.gov (United States)

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Lasso and probabilistic inequalities for multivariate point processes

DEFF Research Database (Denmark)

Hansen, Niels Richard; Reynaud-Bouret, Patricia; Rivoirard, Vincent

2015-01-01

Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select...... for multivariate Hawkes processes are proven, which allows us to check these assumptions by considering general dictionaries based on histograms, Fourier or wavelet bases. Motivated by problems of neuronal activity inference, we finally carry out a simulation study for multivariate Hawkes processes and compare our...... methodology with the adaptive Lasso procedure proposed by Zou in (J. Amer. Statist. Assoc. 101 (2006) 1418–1429). We observe an excellent behavior of our procedure. We rely on theoretical aspects for the essential question of tuning our methodology. Unlike adaptive Lasso of (J. Amer. Statist. Assoc. 101 (2006...

Do continuous assessment results affect final exam outcomes? Evidence from a microeconomics course

Directory of Open Access Journals (Sweden)

Juan Carlos Reboredo

2017-04-01

Full Text Available Continuous assessment aims to enhance student learning and understanding of a subject and so achieve better educational outcomes. We investigated how continuous assessment grades affected final exam grades. Using a dataset for six academic post-Bologna Process years (2009-2015 for a first-year undergraduate microeconomics course offered at a Spanish public university, we examined conditional dependence between continuous assessment and final exam grades. Our results would indicate a limited contribution of continuous assessment results to final exam results: the probability of the final exam performance improving on the continuous assessment grade was lower than the probability of the opposite occurring. A consistent exception, however, was students who obtained an A grade for continuous assessment. Our results would cast some doubt on the beneficial effects of continuous assessment advocated by the Bologna Process.

Multivariate analysis on unilateral cleft lip and palate treatment outcome by EUROCRAN index: A retrospective study.

Science.gov (United States)

Yew, Ching Ching; Alam, Mohammad Khursheed; Rahman, Shaifulizan Abdul

2016-10-01

This study is to evaluate the dental arch relationship and palatal morphology of unilateral cleft lip and palate patients by using EUROCRAN index, and to assess the factors that affect them using multivariate statistical analysis. A total of one hundred and seven patients from age five to twelve years old with non-syndromic unilateral cleft lip and palate were included in the study. These patients have received cheiloplasty and one stage palatoplasty surgery but yet to receive alveolar bone grafting procedure. Five assessors trained in the use of the EUROCRAN index underwent calibration exercise and ranked the dental arch relationships and palatal morphology of the patients' study models. For intra-rater agreement, the examiners scored the models twice, with two weeks interval in between sessions. Variable factors of the patients were collected and they included gender, site, type and, family history of unilateral cleft lip and palate; absence of lateral incisor on cleft side, cheiloplasty and palatoplasty technique used. Associations between various factors and dental arch relationships were assessed using logistic regression analysis. Dental arch relationship among unilateral cleft lip and palate in local population had relatively worse scoring than other parts of the world. Crude logistics regression analysis did not demonstrate any significant associations among the various socio-demographic factors, cheiloplasty and palatoplasty techniques used with the dental arch relationship outcome. This study has limitations that might have affected the results, example: having multiple operators performing the surgeries and the inability to access the influence of underlying genetic predisposed cranio-facial variability. These may have substantial influence on the treatment outcome. The factors that can affect unilateral cleft lip and palate treatment outcome is multifactorial in nature and remained controversial in general. Copyright © 2016 Elsevier Ireland Ltd. All

Power Estimation in Multivariate Analysis of Variance

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Jean François Allaire

2007-09-01

Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

A Major in Science? Initial Beliefs and Final Outcomes for College Major and Dropout

OpenAIRE

Ralph Stinebrickner; Todd R. Stinebrickner

2014-01-01

Taking advantage of unique longitudinal data, we provide the first characterization of what college students believe at the time of entrance about their final major, relate these beliefs to actual major outcomes, and provide an understanding of why students hold the initial beliefs about majors that they do. The data collection and analysis are based directly on a conceptual model in which a student's final major is best viewed as the end result of a learning process. We find that students en...

Predictions of outcomes of renal stones after extracorporeal shock wave lithotripsy from stone characteristics determined by unenhanced helical computed tomography: a multivariate analysis

International Nuclear Information System (INIS)

Wang, Li-Jen; Wong, Yon-Cheong; Chuang, Cheng-Keng; Chu, Sheng-Hsien; Chen, Chih-Shou; Chiang, Yang-Jen; See, Lai-Chu

2005-01-01

The aim of our study is to analyze the relationships between the characteristics of renal stones determined by unenhanced helical computed tomography (UHCT) and their outcomes after extracorporeal shock wave lithotripsy (ESWL) as well as to predict ESWL outcomes of renal stones by their UHCT characteristics with the use of multivariate analysis. During a 7-month period, 80 adult patients with renal stones underwent ESWL as well as UHCT both before and 3 months after ESWL. Of the 80 patients, 42 patients were classified as ESWL successes and 38 as ESWL failures based on their post-ESWL UHCT findings. For pre-ESWL UHCT, a stone number of more than 2 (P=0.0236), a maximal stone size of greater than 12 mm (P 3 (P 3 (P=0.0003), the presence of nonround/oval stones (P=0.0072) and a maximal stone density of more than 900 HU (P=0.0430) were statistically significant predictors of a failure outcome for ESWL. Thus, the analysis of stone characteristics of renal stones by UHCT is helpful in selecting appropriate patients undergoing ESWL for favorable outcomes and reduces the overall costs of the treatment of renal stones. (orig.)

Multivariate Approaches to Classification in Extragalactic Astronomy

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Didier eFraix-Burnet

2015-08-01

Full Text Available Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.

A comparison between multivariate and bivariate analysis used in marketing research

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Constantin, C.

2012-01-01

Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

Science.gov (United States)

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Quality by design case study: an integrated multivariate approach to drug product and process development.

Science.gov (United States)

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

Clustering of samples and elements based on multi-variable chemical data

International Nuclear Information System (INIS)

Op de Beeck, J.

1984-01-01

Clustering and classification are defined in the context of multivariable chemical analysis data. Classical multi-variate techniques, commonly used to interpret such data, are shown to be based on probabilistic and geometrical principles which are not justified for analytical data, since in that case one assumes or expects a system of more or less systematically related objects (samples) as defined by measurements on more or less systematically interdependent variables (elements). For the specific analytical problem of data set concerning a large number of trace elements determined in a large number of samples, a deterministic cluster analysis can be used to develop the underlying classification structure. Three main steps can be distinguished: diagnostic evaluation and preprocessing of the raw input data; computation of a symmetric matrix with pairwise standardized dissimilarity values between all possible pairs of samples and/or elements; and ultrametric clustering strategy to produce the final classification as a dendrogram. The software packages designed to perform these tasks are discussed and final results are given. Conclusions are formulated concerning the dangers of using multivariate, clustering and classification software packages as a black-box

Clinical outcomes after final kissing balloon inflation compared with no final kissing balloon inflation in bifurcation lesions treated with a dedicated coronary bifurcation stent

NARCIS (Netherlands)

M.J. Grundeken (Maik); M. Lesiak (MacIej); S. Asgedom (Solomon); E. Garcia (Eulogio); A. Bethencourt (Armando); M.S. Norell (Michael); K. Damman (Kevin); E. Woudstra (Evert); K. Koch (Karel); M.M. Vis (Marije); J.P.S. Henriques (Jose); J.G.P. Tijssen (Jan); Y. Onuma (Yoshinobu); D.P. Foley (David); A. Bartorelli (Antonio); P.R. Stella (Pieter); R.J. de Winter (Robbert); J.J. Wykrzykowska (Joanna)

2014-01-01

textabstractObjective We evaluated differences in clinical outcomes between patients who underwent final kissing balloon inflation (FKBI) and patients who did not undergo FKBI in bifurcation treatment using the Tryton Side Branch Stent (Tryton Medical, Durham, North Carolina, USA). Methods Clinical

Application of multivariate statistical techniques in microbial ecology.

Science.gov (United States)

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Robust multivariate analysis

CERN Document Server

J Olive, David

2017-01-01

This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

Multivariate return periods of sea storms for coastal erosion risk assessment

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S. Corbella

2012-08-01

Full Text Available The erosion of a beach depends on various storm characteristics. Ideally, the risk associated with a storm would be described by a single multivariate return period that is also representative of the erosion risk, i.e. a 100 yr multivariate storm return period would cause a 100 yr erosion return period. Unfortunately, a specific probability level may be associated with numerous combinations of storm characteristics. These combinations, despite having the same multivariate probability, may cause very different erosion outcomes. This paper explores this ambiguity problem in the context of copula based multivariate return periods and using a case study at Durban on the east coast of South Africa. Simulations were used to correlate multivariate return periods of historical events to return periods of estimated storm induced erosion volumes. In addition, the relationship of the most-likely design event (Salvadori et al., 2011 to coastal erosion was investigated. It was found that the multivariate return periods for wave height and duration had the highest correlation to erosion return periods. The most-likely design event was found to be an inadequate design method in its current form. We explore the inclusion of conditions based on the physical realizability of wave events and the use of multivariate linear regression to relate storm parameters to erosion computed from a process based model. Establishing a link between storm statistics and erosion consequences can resolve the ambiguity between multivariate storm return periods and associated erosion return periods.

Final anatomic and visual outcomes appear independent of duration of silicone oil intraocular tamponade in complex retinal detachment surgery.

Science.gov (United States)

Rhatigan, Maedbh; McElnea, Elizabeth; Murtagh, Patrick; Stephenson, Kirk; Harris, Elaine; Connell, Paul; Keegan, David

2018-01-01

To report anatomic and visual outcomes following silicone oil removal in a cohort of patients with complex retinal detachment, to determine association between duration of tamponade and outcomes and to compare patients with oil removed and those with oil in situ in terms of demographic, surgical and visual factors. We reported a four years retrospective case series of 143 patients with complex retinal detachments who underwent intraocular silicone oil tamponade. Analysis between anatomic and visual outcomes, baseline demographics, duration of tamponade and number of surgical procedures were carried out using Fisher's exact test and unpaired two-tailed t -test. One hundred and six patients (76.2%) had undergone silicone oil removal at the time of review with 96 patients (90.6%) showing retinal reattachment following oil removal. Duration of tamponade was not associated with final reattachment rate or with a deterioration in best corrected visual acuity (BCVA). Patients with oil removed had a significantly better baseline and final BCVA compared to those under oil tamponade ( P =0.0001, <0.0001 respectively). Anatomic and visual outcomes in this cohort are in keeping with those reported in the literature. Favorable outcomes were seen with oil removal but duration of oil tamponade does not affect final attachment rate with modern surgical techniques and should be managed on a case by case basis.

Multivariate calibration applied to the quantitative analysis of infrared spectra

Energy Technology Data Exchange (ETDEWEB)

Haaland, D.M.

1991-01-01

Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

Multivariate Analysis and Prediction of Dioxin-Furan ...

Science.gov (United States)

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Multiphasic perfusion CT in acute middle cerebral artery ischemic stroke: prediction of final infarct volume and correlation with clinical outcome

International Nuclear Information System (INIS)

Yi, Chin A; Na, Dong Gyu; Ryoo, Jae Wook; Moon, Chan Hong; Byun, Hong Sik; Roh, Hong Gee; Moon, Won Jin; Lee, Kwang Ho; Lee, Soo Joo

2002-01-01

To assess the utility of multiphasic perfusion CT in the prediction of final infarct volume, and the relationship between lesion volume revealed by CT imaging and clinical outcome in acute ischemic stroke patients who have not undergone thrombolytic therapy. Thirty-five patients underwent multiphasic perfusion CT within six hours of stroke onset. After baseline unenhanced helical CT scanning, contrast-enhanced CT scans were obtained 20, 34, 48, and 62 secs after the injection of 90 mL contrast medium at a rate of 3 mL/sec. CT peak and total perfusion maps were obtained from serial CT images, and the initial lesion volumes revealed by CT were compared with final infarct volumes and clinical scores. Overall, the lesion volumes seen on CT peak perfusion maps correlated most strongly with final infarct volumes (R2=0.819, p<0.001, slope of regression line=1.016), but individual data showed that they were less than final infarct volume in 31.4% of patients. In those who showed early clinical improvement (n=6), final infarct volume tended to be overestimated by CT peak perfusion mapping and only on total perfusion maps was there significant correlation between lesion volume and final infarct volume (R2=0.854, p=0.008). The lesion volumes depicted by CT maps showed moderate correlation with baseline clinical scores and clinical outcomes (R=0.445-0.706, p≤0.007). CT peak perfusion maps demonstrate strong correlation between lesion volume and final infarct volume, and accurately predict final infarct volume in about two-thirds of the 35 patients. The lesion volume seen on CT maps shows moderate correlation with clinical outcome

The transition from medical student to doctor: perceptions of final year students and preregistration house officers related to expected learning outcomes.

Science.gov (United States)

Lempp, H; Seabrook, M; Cochrane, M; Rees, J

2005-03-01

In this prospective qualitative study over 12 months, we evaluated the educational and clinical effectiveness of a new final year undergraduate programme in a London medical school (Guy's, King's and St Thomas'). A stratified sample of 17/360 final year students were interviewed four times, and the content was assessed against 32 amalgamated learning outcomes identified in 1997 in The New Doctor. At the beginning of the preregistration year, eight of the learning outcomes were already met, 10 partly, eight remained to be attained and for six, insufficient evidence existed. Preregistration house officers who have been through the final year student house officer programme expressed competence in many of the outcomes of the General Medical Council's New Doctor. The study identified areas such as prescribing where further developments are needed and will help in planning the new foundation programme.

Prospective surveillance of multivariate spatial disease data

Science.gov (United States)

Corberán-Vallet, A

2012-01-01

Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429

The mass transfer approach to multivariate discrete first order stochastic dominance

DEFF Research Database (Denmark)

Østerdal, Lars Peter Raahave

2010-01-01

A fundamental result in the theory of stochastic dominance tells that first order dominance between two finite multivariate distributions is equivalent to the property that the one can be obtained from the other by shifting probability mass from one outcome to another that is worse a finite numbe...

Multivariate process monitoring of EAFs

Energy Technology Data Exchange (ETDEWEB)

Sandberg, E.; Lennox, B.; Marjanovic, O.; Smith, K.

2005-06-01

Improved knowledge of the effect of scrap grades on the electric steelmaking process and optimised scrap loading practices increase the potential for process automation. As part of an ongoing programme, process data from four Scandinavian EAFs have been analysed, using the multivariate process monitoring approach, to develop predictive models for end point conditions such as chemical composition, yield and energy consumption. The models developed generally predict final Cr, Ni and Mo and tramp element contents well, but electrical energy consumption, yield and content of oxidisable and impurity elements (C, Si, Mn, P, S) are at present more difficult to predict. Potential scrap management applications of the prediction models are also presented. (author)

Muscle Mass Depletion Associated with Poor Outcome of Sepsis in the Emergency Department.

Science.gov (United States)

Lee, YoonJe; Park, Hyun Kyung; Kim, Won Young; Kim, Myung Chun; Jung, Woong; Ko, Byuk Sung

2018-05-08

Muscle mass depletion has been suggested to predict morbidity and mortality in various diseases. However, it is not well known whether muscle mass depletion is associated with poor outcome in sepsis. We hypothesized that muscle mass depletion is associated with poor outcome in sepsis. Retrospective observational study was conducted in an emergency department during a 9-year period. Medical records of 627 patients with sepsis were reviewed. We divided the patients into 2 groups according to 28-day mortality and compared the presence of muscle mass depletion assessed by the cross-sectional area of the psoas muscle at the level of the third lumbar vertebra on abdomen CT scans. Univariate and multivariate logistic regression analyses were conducted to examine the association of scarcopenia on the outcome of sepsis. A total of 274 patients with sepsis were finally included in the study: 45 (16.4%) did not survive on 28 days and 77 patients (28.1%) were identified as having muscle mass depletion. The presence of muscle mass depletion was independently associated with 28-day mortality on multivariate logistic analysis (OR 2.79; 95% CI 1.35-5.74, p = 0.01). Muscle mass depletion evaluated by CT scan was associated with poor outcome of sepsis patients. Further studies on the appropriateness of specific treatment for muscle mass depletion with sepsis are needed. © 2018 S. Karger AG, Basel.

Multivariate Padé Approximation for Solving Nonlinear Partial Differential Equations of Fractional Order

Directory of Open Access Journals (Sweden)

Veyis Turut

2013-01-01

Full Text Available Two tecHniques were implemented, the Adomian decomposition method (ADM and multivariate Padé approximation (MPA, for solving nonlinear partial differential equations of fractional order. The fractional derivatives are described in Caputo sense. First, the fractional differential equation has been solved and converted to power series by Adomian decomposition method (ADM, then power series solution of fractional differential equation was put into multivariate Padé series. Finally, numerical results were compared and presented in tables and figures.

Multivariate analysis with LISREL

CERN Document Server

Jöreskog, Karl G; Y Wallentin, Fan

2016-01-01

This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

Science.gov (United States)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

Prediction of processing tomato peeling outcomes

Science.gov (United States)

Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After ...

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

Science.gov (United States)

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Multivariate statistical methods a primer

CERN Document Server

Manly, Bryan FJ

2004-01-01

THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o

Prediction of periodically correlated processes by wavelet transform and multivariate methods with applications to climatological data

Science.gov (United States)

Ghanbarzadeh, Mitra; Aminghafari, Mina

2015-05-01

This article studies the prediction of periodically correlated process using wavelet transform and multivariate methods with applications to climatological data. Periodically correlated processes can be reformulated as multivariate stationary processes. Considering this fact, two new prediction methods are proposed. In the first method, we use stepwise regression between the principal components of the multivariate stationary process and past wavelet coefficients of the process to get a prediction. In the second method, we propose its multivariate version without principal component analysis a priori. Also, we study a generalization of the prediction methods dealing with a deterministic trend using exponential smoothing. Finally, we illustrate the performance of the proposed methods on simulated and real climatological data (ozone amounts, flows of a river, solar radiation, and sea levels) compared with the multivariate autoregressive model. The proposed methods give good results as we expected.

Multivariate Frequency-Severity Regression Models in Insurance

Directory of Open Access Journals (Sweden)

Edward W. Frees

2016-02-01

Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.

Can Preoperative Psychological Assessment Predict Outcomes After Temporomandibular Joint Arthroscopy?

Science.gov (United States)

Bouloux, Gary F; Zerweck, Ashley G; Celano, Marianne; Dai, Tian; Easley, Kirk A

2015-11-01

Psychological assessment has been used successfully to predict patient outcomes after cardiothoracic and bariatric surgery. The purpose of this study was to determine whether preoperative psychological assessment could be used to predict patient outcomes after temporomandibular joint arthroscopy. Consecutive patients with temporomandibular dysfunction (TMD) who could benefit from arthroscopy were enrolled in a prospective cohort study. All patients completed the Millon Behavior Medicine Diagnostic survey before surgery. The primary predictor variable was the preoperative psychological scores. The primary outcome variable was the difference in pain between the pre- and postoperative periods. The Spearman rank correlation coefficient and the Pearson product-moment correlation were used to determine the association between psychological factors and change in pain. Univariable and multivariable analyses were performed using a mixed-effects linear model and multiple linear regression. A P value of .05 was considered significant. Eighty-six patients were enrolled in the study. Seventy-five patients completed the study and were included in the final analyses. The mean change in visual analog scale (VAS) pain score 1 month after arthroscopy was -15.4 points (95% confidence interval, -6.0 to -24.7; P psychological factors was identified with univariable correlation analyses. Multivariable analyses identified that a greater pain decrease was associated with a longer duration of preoperative symptoms (P = .054) and lower chronic anxiety (P = .064). This study has identified a weak association between chronic anxiety and the magnitude of pain decrease after arthroscopy for TMD. Further studies are needed to clarify the role of chronic anxiety in the outcome after surgical procedures for the treatment of TMD. Copyright © 2015. Published by Elsevier Inc.

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

Science.gov (United States)

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

Magnesium, hemostasis, and outcomes in patients with intracerebral hemorrhage.

Science.gov (United States)

Liotta, Eric M; Prabhakaran, Shyam; Sangha, Rajbeer S; Bush, Robin A; Long, Alan E; Trevick, Stephen A; Potts, Matthew B; Jahromi, Babak S; Kim, Minjee; Manno, Edward M; Sorond, Farzaneh A; Naidech, Andrew M; Maas, Matthew B

2017-08-22

We tested the hypothesis that admission serum magnesium levels are associated with hematoma volume, hematoma growth, and functional outcomes in patients with intracerebral hemorrhage (ICH). Patients presenting with spontaneous ICH were enrolled in an observational cohort study that prospectively collected demographic, clinical, laboratory, radiographic, and outcome data. We performed univariate and adjusted multivariate analyses to assess for associations between serum magnesium levels and initial hematoma volume, final hematoma volume, and in-hospital hematoma growth as radiographic measures of hemostasis, and functional outcome measured by the modified Rankin Scale (mRS) at 3 months. We included 290 patients for analysis. Admission serum magnesium was 2.0 ± 0.3 mg/dL. Lower admission magnesium levels were associated with larger initial hematoma volumes on univariate ( p = 0.02), parsimoniously adjusted ( p = 0.002), and fully adjusted models ( p = 0.006), as well as greater hematoma growth ( p = 0.004, p = 0.005, and p = 0.008, respectively) and larger final hematoma volumes ( p = 0.02, p = 0.001, and p = 0.002, respectively). Lower admission magnesium level was associated with worse functional outcomes at 3 months (i.e., higher mRS; odds ratio 0.14, 95% confidence interval 0.03-0.64, p = 0.011) after adjustment for age, admission Glasgow Coma Scale score, initial hematoma volume, time from symptom onset to initial CT, and hematoma growth, with evidence that the effect of magnesium is mediated through hematoma growth. These data support the hypothesis that magnesium exerts a clinically meaningful influence on hemostasis in patients with ICH. © 2017 American Academy of Neurology.

Multivariate statistics exercises and solutions

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

Classification of Specialized Farms Applying Multivariate Statistical Methods

Directory of Open Access Journals (Sweden)

Zuzana Hloušková

2017-01-01

Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

A joint model for multivariate hierarchical semicontinuous data with replications.

Science.gov (United States)

Kassahun-Yimer, Wondwosen; Albert, Paul S; Lipsky, Leah M; Nansel, Tonja R; Liu, Aiyi

2017-01-01

Longitudinal data are often collected in biomedical applications in such a way that measurements on more than one response are taken from a given subject repeatedly overtime. For some problems, these multiple profiles need to be modeled jointly to get insight on the joint evolution and/or association of these responses over time. In practice, such longitudinal outcomes may have many zeros that need to be accounted for in the analysis. For example, in dietary intake studies, as we focus on in this paper, some food components are eaten daily by almost all subjects, while others are consumed episodically, where individuals have time periods where they do not eat these components followed by periods where they do. These episodically consumed foods need to be adequately modeled to account for the many zeros that are encountered. In this paper, we propose a joint model to analyze multivariate hierarchical semicontinuous data characterized by many zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components. The novelty of the proposed approach is that it incorporates: (1) multivariate, possibly correlated, response variables; (2) within subject correlation resulting from repeated measurements taken from each subject; (3) many zero observations; (4) overdispersion; and (5) replicate measurements at each visit time.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

Directory of Open Access Journals (Sweden)

Carlos E. Galván-Tejada

2017-02-01

Full Text Available Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

Science.gov (United States)

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

UN NUEVO ENFOQUE DEL CONTROL POR MODO DESLIZANTE PARA SISTEMAS MULTIVARIABLES

Directory of Open Access Journals (Sweden)

Rafael Angel Orellana Prato

2016-09-01

Full Text Available Este trabajo presenta el diseño de un sistema de control por modo deslizante (SMCr basado en alimentación adelantada (feedforward aplicado a un sistema multivariable, sintonizado para especificaciones de la respuesta transitoria, usando modelos de primer orden más tiempo muerto (POMTM. Como caso de estudio se presenta el modelo multivariable de dos entradas y dos salidas de una columna de destilación Wood and Berry, la cual presenta un alto índice de interacción. Las pruebas demostraron que la estrategia de control propuesta mejora el desempeño de un sistema de control multivariable ante cambios en los valores de referencia y rechazo a las perturbaciones, sin embargo, el tiempo de establecimiento de las variables controladas aumenta producto de dinámica considerada en el desacoplamiento. Se comprobó que en sistemas con alta interacción las señales de control tienen cambios más suaves lo que indica menor desgaste en los elementos finales de control.

Impact of functional and structural social relationships on two year depression outcomes: A multivariate analysis.

Science.gov (United States)

Davidson, Sandra K; Dowrick, Christopher F; Gunn, Jane M

2016-03-15

High rates of persistent depression highlight the need to identify the risk factors associated with poor depression outcomes and to provide targeted interventions to people at high risk. Although social relationships have been implicated in depression course, interventions targeting social relationships have been disappointing. Possibly, interventions have targeted the wrong elements of relationships. Alternatively, the statistical association between relationships and depression course is not causal, but due to shared variance with other factors. We investigated whether elements of social relationships predict major depressive episode (MDE) when multiple relevant variables are considered. Data is from a longitudinal study of primary care patients with depressive symptoms. 494 participants completed questionnaires at baseline and a depression measure (PHQ-9) two years later. Baseline measures included functional (i.e. quality) and structural (i.e. quantity) social relationships, depression, neuroticism, chronic illness, alcohol abuse, childhood abuse, partner violence and sociodemographic characteristics. Logistic regression with generalised estimating equations was used to estimate the association between social relationships and MDE. Both functional and structural social relationships predicted MDE in univariate analysis. Only functional social relationships remained significant in multivariate analysis (OR: 0.87; 95%CI: 0.79-0.97; p=0.01). Other unique predictors of MDE were baseline depression severity, neuroticism, childhood sexual abuse and intimate partner violence. We did not assess how a person's position in their depression trajectory influenced the association between social relationships and depression. Interventions targeting relationship quality may be part of a personalised treatment plan for people at high risk due of persistent depression due to poor social relationships. Copyright © 2015 Elsevier B.V. All rights reserved.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

Science.gov (United States)

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Multivariate covariance generalized linear models

DEFF Research Database (Denmark)

Bonat, W. H.; Jørgensen, Bent

2016-01-01

are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

Multivariate extended skew-t distributions and related families

KAUST Repository

Arellano-Valle, Reinaldo B.

2010-12-01

A class of multivariate extended skew-t (EST) distributions is introduced and studied in detail, along with closely related families such as the subclass of extended skew-normal distributions. Besides mathematical tractability and modeling flexibility in terms of both skewness and heavier tails than the normal distribution, the most relevant properties of the EST distribution include closure under conditioning and ability to model lighter tails as well. The first part of the present paper examines probabilistic properties of the EST distribution, such as various stochastic representations, marginal and conditional distributions, linear transformations, moments and in particular Mardia’s measures of multivariate skewness and kurtosis. The second part of the paper studies statistical properties of the EST distribution, such as likelihood inference, behavior of the profile log-likelihood, the score vector and the Fisher information matrix. Especially, unlike the extended skew-normal distribution, the Fisher information matrix of the univariate EST distribution is shown to be non-singular when the skewness is set to zero. Finally, a numerical application of the conditional EST distribution is presented in the context of confidential data perturbation.

Multivariate extended skew-t distributions and related families

KAUST Repository

Arellano-Valle, Reinaldo B.; Genton, Marc G.

2010-01-01

A class of multivariate extended skew-t (EST) distributions is introduced and studied in detail, along with closely related families such as the subclass of extended skew-normal distributions. Besides mathematical tractability and modeling flexibility in terms of both skewness and heavier tails than the normal distribution, the most relevant properties of the EST distribution include closure under conditioning and ability to model lighter tails as well. The first part of the present paper examines probabilistic properties of the EST distribution, such as various stochastic representations, marginal and conditional distributions, linear transformations, moments and in particular Mardia’s measures of multivariate skewness and kurtosis. The second part of the paper studies statistical properties of the EST distribution, such as likelihood inference, behavior of the profile log-likelihood, the score vector and the Fisher information matrix. Especially, unlike the extended skew-normal distribution, the Fisher information matrix of the univariate EST distribution is shown to be non-singular when the skewness is set to zero. Finally, a numerical application of the conditional EST distribution is presented in the context of confidential data perturbation.

A review of multivariate analyses in imaging genetics

Directory of Open Access Journals (Sweden)

Jingyu eLiu

2014-03-01

Full Text Available Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a prior driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA, and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and

Multivariate Statistical Process Control Charts: An Overview

OpenAIRE

Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John

2006-01-01

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...

Methods of Multivariate Analysis

CERN Document Server

Rencher, Alvin C

2012-01-01

Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

Continuous multivariate exponential extension

International Nuclear Information System (INIS)

Block, H.W.

1975-01-01

The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE

Emulating facial biomechanics using multivariate partial least squares surrogate models

OpenAIRE

Martens, Harald; Wu, Tim; Hunter, Peter; Mithraratne, Kumar

2014-01-01

This is the author’s final, accepted and refereed manuscript to the article. Locked until 2015-05-06 A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-mode...

The multivariate supOU stochastic volatility model

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole; Stelzer, Robert

Using positive semidefinite supOU (superposition of Ornstein-Uhlenbeck type) processes to describe the volatility, we introduce a multivariate stochastic volatility model for financial data which is capable of modelling long range dependence effects. The finiteness of moments and the second order...... structure of the volatility, the log returns, as well as their "squares" are discussed in detail. Moreover, we give several examples in which long memory effects occur and study how the model as well as the simple Ornstein-Uhlenbeck type stochastic volatility model behave under linear transformations....... In particular, the models are shown to be preserved under invertible linear transformations. Finally, we discuss how (sup)OU stochastic volatility models can be combined with a factor modelling approach....

Advanced event reweighting using multivariate analysis

International Nuclear Information System (INIS)

Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J

2012-01-01

Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.

An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

DEFF Research Database (Denmark)

Bladt, Mogens; Nielsen, Bo Friis

2012-01-01

Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...

Supporting inquiry learning by promoting normative understanding of multivariable causality

Science.gov (United States)

Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

Multivariate Birkhoff interpolation

CERN Document Server

Lorentz, Rudolph A

1992-01-01

The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...

Long-term outcomes of penetrating keratoplasty in keratoconus:analysis of the factors associated with final visual acuities

Directory of Open Access Journals (Sweden)

Jin A Choi

2014-06-01

Full Text Available AIM: To investigate the long-term results of penetrating keratoplasty (PK in patients with keratoconus (KC and to evaluate factors that might influence the final visual outcome.METHODS:We retrospectively reviewed the data of all patients with clinical KC who had undergone PK by a single corneal surgeon in a single center from May 1980 to December 2005. The age of the patients, preoperative best-corrected visual acuity (BCVA, corneal thickness, death to preservation time, and preservation to transplantation time were recorded. Additionally, postoperative complications such as graft rejection, development of glaucoma and specular microscopy were checked during the follow-up.RESULTS:Sixty-nine eyes from 69 patients were finally included. The follow-up period was 8.64±6.13y. Graft rejection occurred in 4 eyes of 69 cases (5.8%, and the time to graft rejection was 2.1±1.3y. A Kaplan–Meier survival analysis showed that the estimated cumulative probability of graft rejection at 6, 13, and 17y after PK were 95.6%, 90.0%, and 78.8%, respectively. When we evaluated factors that might influence final BCVA in eyes, no disparity donor-host trephine size (same graft size as well as higher spherical equivalent, and average K-value were associated with higher final BCVA. (P=0.006, 0.051, 0.092, and 0.021 in eyes with follow-up <8y; P=0.068, 0.065, and 0.030 in eyes with follow-up ≥8y, respectively.CONCLUSION: The long-term results of PK in patients with KC were favorable with a high percentage of good BCVA. Less myopic change and low average K-reading, as well as a surgical technique using the same size donor-recipient button may provide better visual outcomes particularly in patients with KC.

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

Science.gov (United States)

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

Clustering Multivariate Time Series Using Hidden Markov Models

Directory of Open Access Journals (Sweden)

Shima Ghassempour

2014-03-01

Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

Comparative analysis of rationale used by dentists and patient for final esthetic outcome of dental treatment.

Science.gov (United States)

Reddy, S Varalakshmi; Madineni, Praveen Kumar; Sudheer, A; Gujjarlapudi, Manmohan Choudary; Sreedevi, B; Reddy, Patelu Sunil Kumar

2013-05-01

To compare and evaluate the perceptions of esthetics among dentists and patients regarding the final esthetic outcome of a dental treatment. Esthetics is a matter of perception and is associated with the way different people look at an object. What constitutes esthetic for a particular person may not be acceptable for another. Hence it is subjective in nature. This becomes more obvious during the post-treatment evaluation of esthetics by dentist and the concerned patient. Opinion seldom matches. Hence, the study is a necessary part of the process of understanding the mind of dentist and patient regarding what constitutes esthetics. A survey has been conducted by means of a questionnaire consisting of 10 questions, on two groups of people. First group consists of 100 dentists picked at random in Kanyakumari district of Tamil Nadu, India. Second group consisted of 100 patients who required complete denture prosthesis. The second group was divided into two subgroups A and B. Subgroup A consisting of 50 men and subgroup B consisting of 50 women. In each subgroup 25 patients were selected in age group of 40 to 50 and 25 patients were selected in the age group of 50 to 60. The questionnaire was given to both the groups and asked to fill up, which was then statistically analyzed to look for patterns of thought process among them. Results were subjected to statistical analysis by Student's t-test. Perceptions of esthetics differs from dentist who is educated regarding esthetic principles of treatment and a patient who is not subjected to such education. Since, the questions were formulated such that patients could better understand the underlying problem, the final outcome of survey is a proof that dentists need to take into account what the patient regards as esthetics in order to provide a satisfactory treatment. CLINICAL AND ACADEMIC SIGNIFICANCE: The current study helps the dentist to better educate the patient regarding esthetics so that patient appreciates the final

Multivariate meta-analysis: Potential and promise

Science.gov (United States)

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Multivariate refined composite multiscale entropy analysis

International Nuclear Information System (INIS)

Humeau-Heurtier, Anne

2016-01-01

Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

Multivariate Generalized Multiscale Entropy Analysis

Directory of Open Access Journals (Sweden)

Anne Humeau-Heurtier

2016-11-01

Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

Robust adaptive multivariable higher-order sliding mode flight control for air-breathing hypersonic vehicle with actuator failures

Directory of Open Access Journals (Sweden)

Peng Li

2016-10-01

Full Text Available This article proposes an adaptive multivariable higher-order sliding mode control for the longitudinal model of an air-breathing vehicle under system uncertainties and actuator failures. Firstly, a fast finite-time control law is designed for a chain of integrators. Secondly, based on the input/output feedback linearization technique, the system uncertainty and external disturbances are modeled as additive certainty and the actuator failures are modeled as multiplicative uncertainty. By using the proposed fast finite-time control law, a robust multivariable higher-order sliding mode control is designed for the air-breathing hypersonic vehicle with actuator failures. Finally, adaptive laws are proposed for the adaptation of the parameters in the robust multivariable higher-order sliding mode control. Thus, the bounds of the uncertainties are not needed in the control system design. Simulation results show the effectiveness of the proposed robust adaptive multivariable higher-order sliding mode control.

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

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Kun Liu

2015-01-01

Full Text Available The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body’s standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age.

Multivariate pattern dependence.

Directory of Open Access Journals (Sweden)

Stefano Anzellotti

2017-11-01

Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

Science.gov (United States)

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-06

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-01

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

A primer of multivariate statistics

CERN Document Server

Harris, Richard J

2014-01-01

Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why

Multivariate stochastic simulation with subjective multivariate normal distributions

Science.gov (United States)

P. J. Ince; J. Buongiorno

1991-01-01

In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

Model Checking Multivariate State Rewards

DEFF Research Database (Denmark)

Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis

2010-01-01

We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...

Sequential computerized tomography changes and related final outcome in severe head injury patients

International Nuclear Information System (INIS)

Lobato, R.D.; Gomez, P.A.; Alday, R.

1997-01-01

The authors analyzed the serial computerized tomography (CT) findings in a large series of severely head injured patients in order to assess the variability in gross intracranial pathology through the acute posttraumatic period and determine the most common patterns of CT change. A second aim was to compare the prognostic significance of the different CT diagnostic categories used in the study (Traumatic Coma Data Bank CT pathological classification) when gleaned either from the initial (postadmission) or the control CT scans, and determine the extent to which having a second CT scan provides more prognostic information than only one scan. 92 patients (13.3 % of the total population) died soon after injury. Of the 587 who survived long enough to have at least one control CT scan 23.6 % developed new diffuse brain swelling, and 20.9 % new focal mass lesions most of which had to be evacuated. The relative risk for requiring a delayed operation as related to the diagnostic category established by using the initial CT scans was by decreasing order: diffuse injury IV (30.7 %), diffuse injury III (30.5 %), non evacuated mass (20 %), evacuated mass (20.2 %), diffuse injury II (12.1 %), and diffuse injury I (8.6 %). Overall, 51.2 % of the patients developed significant CT changes (for worse or better) occurring either spontaneously or following surgery, and their final outcomes were more closely related to the control than to the initial CT diagnoses. In fact, the final outcome was more accurately predicted by using the control CT scans (81.2 % of the cases) than by using the initial CT scans (71.5 % of the cases only). Since the majority of relevant CT changes developed within 48 hours after injury a pathological categorization made by using an early control CT scan seems to be most useful for prognostic purposes. Prognosis associated with the CT pathological categories used in the study was similar independently of the moment of the acute posttraumatic period at which

Self-declared stock ownership and association with positive trial outcome in randomized controlled trials with binary outcomes published in general medical journals: a cross-sectional study.

Science.gov (United States)

Falk Delgado, Alberto; Falk Delgado, Anna

2017-07-26

Describe the prevalence and types of conflicts of interest (COI) in published randomized controlled trials (RCTs) in general medical journals with a binary primary outcome and assess the association between conflicts of interest and favorable outcome. Parallel-group RCTs with a binary primary outcome published in three general medical journals during 2013-2015 were identified. COI type, funding source, and outcome were extracted. Binomial logistic regression model was performed to assess association between COI and funding source with outcome. A total of 509 consecutive parallel-group RCTs were included in the study. COI was reported in 74% in mixed funded RCTs and in 99% in for-profit funded RCTs. Stock ownership was reported in none of the non-profit RCTs, in 7% of mixed funded RCTs, and in 50% of for-profit funded RCTs. Mixed-funded RCTs had employees from the funding company in 11% and for-profit RCTs in 76%. Multivariable logistic regression revealed that stock ownership in the funding company among any of the authors was associated with a favorable outcome (odds ratio = 3.53; 95% confidence interval = 1.59-7.86; p < 0.01). COI in for-profit funded RCTs is extensive, because the factors related to COI are not fully independent, a multivariable analysis should be cautiously interpreted. However, after multivariable adjustment only stock ownership from the funding company among authors is associated with a favorable outcome.

[Severe Adverse Pregnancy Outcomes in Placenta Previa and Prior Cesarean Delivery].

Science.gov (United States)

Zhou, Mi; Chen, Meng; Zhang, Li; He, Guo-Lin; He, Lei; Wei, Qiang; Li, Tao; Liu, Xing-Hui

2017-09-01

To investigate the severe adverse pregnancy outcomes in pregnancies with placenta previa and prior cesarean delivery and its risk factors. This retrospective casecontrol study reviewed all pregnancies with placenta previa and prior cesarean delivery delivered by repeat cesarean section in our institution between January 2005 and June 2015,and investigated the incidence of severe adverse pregnancy outcome. A composite of severe adverse pregnancy outcomes (including transfusion of 10 units or more red blood cells,maternal ICU admission,unanticipated injuries,repeat operation,hysterectomy,and maternal death) and other maternal and neonatal outcomes were described. Univariate and multivariable logistic regression analysis were used to quantify the effects of risk factors on severe adverse pregnancy outcomes. There were 478 women with placenta previa and prior cesarean delivery in our hospital over the last decade. The average age of them was 32.5±4.8 years old,most women were beyond 30 years old,the average gravidity and parity were 4 and 1,131 cases (27.4%) had severe adverse pregnancy outcomes. Transfusion of 10 units or more red blood cells happened in 75 cases (15.7%,75/478); 44 cases (9.2%,44/478) necessitated maternal ICU admission; unanticipated bladder injury occurred in 11 cases,but non ureter or bowel injury happened; All 4 repeat operations were due to delayed hemorrhage after conservative management during cesarean delivery,and an emergent hysterectomy was performed for all of the 4 cases. Hysterectomy (107 cases,22.4%) was the most common severe adverse pregnancy outcome. Among all 311 morbidly adherent placenta cases finally confirmed by pathological or surgical findings or both,only 172 (55.3%) were suspected before delivery. Multivariable logistic regression analysis showed that the risk of severe adverse pregnancy outcomes was significantly increased by pernicious placenta previa (i.e. anterior placenta overlying the prior cesarean scar),suspicion of

Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples

DEFF Research Database (Denmark)

Jensen, Kristina Nedenskov; Jessen, Flemming; Jørgensen, Bo

2008-01-01

One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only...... on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available...... and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified...

Multivariate strategies in functional magnetic resonance imaging

DEFF Research Database (Denmark)

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a `mind reading' predictive multivariate fMRI model....

Applied multivariate statistical analysis

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

Multivariate Bonferroni-type inequalities theory and applications

CERN Document Server

Chen, John

2014-01-01

Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamil

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

Scattering amplitudes from multivariate polynomial division

Energy Technology Data Exchange (ETDEWEB)

Mastrolia, Pierpaolo, E-mail: pierpaolo.mastrolia@cern.ch [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Dipartimento di Fisica e Astronomia, Universita di Padova, Padova (Italy); INFN Sezione di Padova, via Marzolo 8, 35131 Padova (Italy); Mirabella, Edoardo, E-mail: mirabell@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Ossola, Giovanni, E-mail: GOssola@citytech.cuny.edu [New York City College of Technology, City University of New York, 300 Jay Street, Brooklyn, NY 11201 (United States); Graduate School and University Center, City University of New York, 365 Fifth Avenue, New York, NY 10016 (United States); Peraro, Tiziano, E-mail: peraro@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany)

2012-11-15

We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Groebner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.

Multivariate Matrix-Exponential Distributions

DEFF Research Database (Denmark)

Bladt, Mogens; Nielsen, Bo Friis

2010-01-01

be written as linear combinations of the elements in the exponential of a matrix. For this reason we shall refer to multivariate distributions with rational Laplace transform as multivariate matrix-exponential distributions (MVME). The marginal distributions of an MVME are univariate matrix......-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...

Multivariate analysis methods in physics

International Nuclear Information System (INIS)

Wolter, M.

2007-01-01

A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

Science.gov (United States)

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies

DEFF Research Database (Denmark)

Tybjærg-Hansen, Anne

2009-01-01

Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements...... of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study......-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies...

Method for statistical data analysis of multivariate observations

CERN Document Server

Gnanadesikan, R

1997-01-01

A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

Multivariate survival analysis and competing risks

CERN Document Server

Crowder, Martin J

2012-01-01

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

The value of multivariate model sophistication

DEFF Research Database (Denmark)

Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco

2014-01-01

We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...

Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data

Directory of Open Access Journals (Sweden)

Arnošt Komárek

2014-09-01

Full Text Available R package mixAK originally implemented routines primarily for Bayesian estimation of finite normal mixture models for possibly interval-censored data. The functionality of the package was considerably enhanced by implementing methods for Bayesian estimation of mixtures of multivariate generalized linear mixed models proposed in Komrek and Komrkov (2013. Among other things, this allows for a cluster analysis (classification based on multivariate continuous and discrete longitudinal data that arise whenever multiple outcomes of a different nature are recorded in a longitudinal study. This package also allows for a data-driven selection of a number of clusters as methods for selecting a number of mixture components were implemented. A model and clustering methodology for multivariate continuous and discrete longitudinal data is overviewed. Further, a step-by-step cluster analysis based jointly on three longitudinal variables of different types (continuous, count, dichotomous is given, which provides a user manual for using the package for similar problems.

Expression of the Hippo transducer TAZ in association with WNT pathway mutations impacts survival outcomes in advanced gastric cancer patients treated with first-line chemotherapy.

Science.gov (United States)

Melucci, Elisa; Casini, Beatrice; Ronchetti, Livia; Pizzuti, Laura; Sperati, Francesca; Pallocca, Matteo; De Nicola, Francesca; Goeman, Frauke; Gallo, Enzo; Amoreo, Carla Azzurra; Sergi, Domenico; Terrenato, Irene; Vici, Patrizia; Di Lauro, Luigi; Diodoro, Maria Grazia; Pescarmona, Edoardo; Barba, Maddalena; Mazzotta, Marco; Mottolese, Marcella; Fanciulli, Maurizio; Ciliberto, Gennaro; De Maria, Ruggero; Buglioni, Simonetta; Maugeri-Saccà, Marcello

2018-02-05

An extensive crosstalk co-regulates the Hippo and Wnt pathway. Preclinical studies revealed that the Hippo transducers YAP/TAZ mediate a number of oncogenic functions in gastric cancer (GC). Moreover, comprehensive characterization of GC demonstrated that the Wnt pathway is targeted by oncogenic mutations. On this ground, we hypothesized that YAP/TAZ- and Wnt-related biomarkers may predict clinical outcomes in GC patients treated with chemotherapy. In the present study, we included 86 patients with advanced GC treated with first-line chemotherapy in prospective phase II trials or in routine clinical practice. Tissue samples were immunostained to evaluate the expression of YAP/TAZ. Mutational status of key Wnt pathway genes (CTNNB1, APC and FBXW7) was assessed by targeted DNA next-generation sequencing (NGS). Survival curves were estimated and compared by the Kaplan-Meier product-limit method and the log-rank test, respectively. Variables potentially affecting progression-free survival (PFS) were verified in univariate Cox proportional hazard models. The final multivariate Cox models were obtained with variables testing significant at the univariate analysis, and by adjusting for all plausible predictors of the outcome of interest (PFS). We observed a significant association between TAZ expression and Wnt mutations (Chi-squared p = 0.008). Combined TAZ expression and Wnt mutations (TAZ pos /WNT mut ) was more frequently observed in patients with the shortest progression-free survival (negative outliers) (Fisher p = 0.021). Uni-and multivariate Cox regression analyses revealed that patients whose tumors harbored the TAZ pos /WNT mut signature had an increased risk of disease progression (univariate Cox: HR 2.27, 95% CI 1.27-4.05, p = 0.006; multivariate Cox: HR 2.73, 95% CI 1.41-5.29, p = 0.003). Finally, the TAZ pos /WNT mut signature negatively impacted overall survival. Collectively, our findings indicate that the oncogenic YAP/TAZ-Wnt crosstalk may be

Multivariate statistical methods a first course

CERN Document Server

Marcoulides, George A

2014-01-01

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin

Overweight and obesity in patients with atrial fibrillation: Sex differences in 1-year outcomes in the EORP-AF General Pilot Registry.

Science.gov (United States)

Boriani, Giuseppe; Laroche, Cécile; Diemberger, Igor; Fantecchi, Elisa; Meeder, Joan; Kurpesa, Malgorzata; Baluta, Monica Mariana; Proietti, Marco; Tavazzi, Luigi; Maggioni, Aldo P; Lip, Gregory Y H

2018-04-01

The impact of overweight and obesity on outcomes in "real world" patients with atrial fibrillation (AF) is not fully defined. Second, sex differences in AF outcomes may also exist. The aim was to investigate outcomes at 1 year follow-up for AF patients enrolled in the EORP-AF Registry, according to BMI (kg/m 2 ), comparing patients with normal BMI (18.5 to obesity (≥ 30 kg/m 2 ), in relation to sex differences. Among 2,540 EORP AF patients (38.9% female; median age 69) with 1 year follow-up data available, 720 (28.3%) had a normal BMI, 1,084 (42.7%) were overweight, and 736 (29.0%) were obese. Obese patients were younger and with more prevalent diabetes mellitus and hypertension (P different according to BMI among female patients (9.3% normal BMI, 5.3% overweight, and 4.3 % obese, P = 0.023), but not among male patients (P = 0.748). The composite outcome of thromboembolic events and death was also significantly different, being lower in obese females (P = 0.035). Among male patients, bleeding events were significantly more frequent in obese subjects (P = 0.035). On multivariable Cox analysis, BMI was not independently associated with all-cause mortality. Among AF patients, overweight and obesity are common and associated with better outcomes in females (a finding previously reported as "obesity paradox"), while no significant differences in outcomes are detected among male patients. Final multivariable model found that increasing BMI was not associated with increased risk of all-cause death; conversely, age and comorbidities persisted as major determinants. © 2018 Wiley Periodicals, Inc.

Multivariable control in nuclear power stations

International Nuclear Information System (INIS)

Parent, M.; McMorran, P.D.

1982-11-01

Multivariable methods have the potential to improve the control of large systems such as nuclear power stations. Linear-quadratic optimal control is a multivariable method based on the minimization of a cost function. A related technique leads to the Kalman filter for estimation of plant state from noisy measurements. A design program for optimal control and Kalman filtering has been developed as part of a computer-aided design package for multivariable control systems. The method is demonstrated on a model of a nuclear steam generator, and simulated results are presented

Determinants of Outcome of Final Undergraduate Surgery ...

African Journals Online (AJOL)

2018-06-11

Jun 11, 2018 ... Female gender (P < 0.001), passing CA (P < 0.001), and shorter duration‑<9 years in medical school (P < 0.001) were strongly associated with passing the final surgery ... Conclusion: CA is the single most important determinant of ... disadvantages of the traditional clinical examinations.[10]. Since then ...

Clinical predictors of outcome in patients with inflammatory dilated cardiomyopathy.

Directory of Open Access Journals (Sweden)

Konstantinos Karatolios

Full Text Available The study objectives were to identify predictors of outcome in patients with inflammatory dilated cardiomyopathy (DCMi.From 2004 to 2008, 55 patients with biopsy-proven DCMi were identified and followed up for 58.2±19.8 months. Predictors of outcome were identified in a multivariable analysis with a Cox proportional hazards analysis. The primary endpoint was a composite of death, heart transplantation and hospitalization for heart failure or ventricular arrhythmias.For the primary endpoint, a QTc interval >440msec (HR 2.84; 95% CI 1.03-7.87; p = 0.044, a glomerular filtration rate (GFR 440msec, a GFR<60ml/min/1.73m2 and worsening of NYHA classification during follow-up were univariate predictors of adverse prognosis. In contrast, NYHA classification at baseline, left ventricular ejection fraction, atrial fibrillation, treatment with digitalis or viral genome detection were not related to outcome. After multivariable analysis, a GFR <60ml/min/1.73m2 remained independently associated with adverse outcome.

Patient characteristics of smokers undergoing lumbar spine surgery: an analysis from the Quality Outcomes Database.

Science.gov (United States)

Asher, Anthony L; Devin, Clinton J; McCutcheon, Brandon; Chotai, Silky; Archer, Kristin R; Nian, Hui; Harrell, Frank E; McGirt, Matthew; Mummaneni, Praveen V; Shaffrey, Christopher I; Foley, Kevin; Glassman, Steven D; Bydon, Mohamad

2017-12-01

OBJECTIVE In this analysis the authors compare the characteristics of smokers to nonsmokers using demographic, socioeconomic, and comorbidity variables. They also investigate which of these characteristics are most strongly associated with smoking status. Finally, the authors investigate whether the association between known patient risk factors and disability outcome is differentially modified by patient smoking status for those who have undergone surgery for lumbar degeneration. METHODS A total of 7547 patients undergoing degenerative lumbar surgery were entered into a prospective multicenter registry (Quality Outcomes Database [QOD]). A retrospective analysis of the prospectively collected data was conducted. Patients were dichotomized as smokers (current smokers) and nonsmokers. Multivariable logistic regression analysis fitted for patient smoking status and subsequent measurement of variable importance was performed to identify the strongest patient characteristics associated with smoking status. Multivariable linear regression models fitted for 12-month Oswestry Disability Index (ODI) scores in subsets of smokers and nonsmokers was performed to investigate whether differential effects of risk factors by smoking status might be present. RESULTS In total, 18% (n = 1365) of patients were smokers and 82% (n = 6182) were nonsmokers. In a multivariable logistic regression analysis, the factors significantly associated with patients' smoking status were sex (p smoker (p = 0.0008), while patients with coronary artery disease had greater odds of being a smoker (p = 0.044). Patients' propensity for smoking was also significantly associated with higher American Society of Anesthesiologists (ASA) class (p smokers and nonsmokers. CONCLUSIONS Using a large, national, multiinstitutional registry, the authors described the profile of patients who undergo lumbar spine surgery and its association with their smoking status. Compared with nonsmokers, smokers were younger, male

A general, multivariate definition of causal effects in epidemiology.

Science.gov (United States)

Flanders, W Dana; Klein, Mitchel

2015-07-01

Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

Lectures in feedback design for multivariable systems

CERN Document Server

Isidori, Alberto

2017-01-01

This book focuses on methods that relate, in one form or another, to the “small-gain theorem”. It is aimed at readers who are interested in learning methods for the design of feedback laws for linear and nonlinear multivariable systems in the presence of model uncertainties. With worked examples throughout, it includes both introductory material and more advanced topics. Divided into two parts, the first covers relevant aspects of linear-systems theory, the second, nonlinear theory. In order to deepen readers’ understanding, simpler single-input–single-output systems generally precede treatment of more complex multi-input–multi-output (MIMO) systems and linear systems precede nonlinear systems. This approach is used throughout, including in the final chapters, which explain the latest advanced ideas governing the stabilization, regulation, and tracking of nonlinear MIMO systems. Two major design problems are considered, both in the presence of model uncertainties: asymptotic stabilization with a “...

Factors that impact the outcome of endoscopic correction of vesicoureteral reflux: a multivariate analysis.

Science.gov (United States)

Kajbafzadeh, Abdol-Mohammad; Tourchi, Ali; Aryan, Zahra

2013-02-01

To identify independent factors that may predict vesicoureteral reflux (VUR) resolution after endoscopic treatment using dextranomer/hyaluronic acid copolymer (Deflux) in children free of anatomical anomalies. A retrospective study was conducted in our pediatric referral center from 1998 to 2011 on children with primary VUR who underwent endoscopic injection of Deflux with or without concomitant autologous blood injection (called HABIT or HIT, respectively). Children with secondary VUR or incomplete records were excluded from the study. Potential factors were divided into three categories including preoperative, intraoperative and postoperative. Success was defined as no sign of VUR on postoperative voiding cystourethrogram. Univariate and multivariate logistic regression models were constructed to identify independent factors that may predict success. Odds ratio (OR) and 95 % confidence interval (95 % CI) for prediction of success were estimated for each factor. From 485 children received Deflux injection, a total of 372 with a mean age of 3.10 years (ranged from 6 months to 12 years) were included in the study and endoscopic management was successful in 322 (86.6 %) of them. Of the patients, 185 (49.7 %) underwent HIT and 187 (50.3 %) underwent HABIT technique. On univariate analysis, VUR grade from preoperative category (OR = 4.79, 95 % CI = 2.22-10.30, p = 0.000), operation technique (OR = 0.33, 95 % CI = 0.17-0.64, p = 0.001) and presence of mound on postoperative sonography (OR = 0.06, 95 % CI = 0.02-0.16, p = 0.000) were associated with success. On multivariate analysis, preoperative VUR grade (OR = 4.85, 95 % CI = 2.49-8.96, p = 0.000) and identification of mound on postoperative sonography (OR = 0.07, 95 % CI = 0.01-0.18, p = 0.000) remained as independent success predictors. Based on this study, successful VUR correction after the endoscopic injection of Deflux can be predicted with respect to preoperative VUR grade and presence of mound after operation.

Final analysis of survival outcomes in the phase 3 FIRST trial of up-front treatment for multiple myeloma.

Science.gov (United States)

Facon, Thierry; Dimopoulos, Meletios A; Dispenzieri, Angela; Catalano, John V; Belch, Andrew; Cavo, Michele; Pinto, Antonello; Weisel, Katja; Ludwig, Heinz; Bahlis, Nizar J; Banos, Anne; Tiab, Mourad; Delforge, Michel; Cavenagh, Jamie D; Geraldes, Catarina; Lee, Je-Jung; Chen, Christine; Oriol, Albert; De La Rubia, Javier; White, Darrell; Binder, Daniel; Lu, Jin; Anderson, Kenneth C; Moreau, Philippe; Attal, Michel; Perrot, Aurore; Arnulf, Bertrand; Qiu, Lugui; Roussel, Murielle; Boyle, Eileen; Manier, Salomon; Mohty, Mohamad; Avet-Loiseau, Herve; Leleu, Xavier; Ervin-Haynes, Annette; Chen, Guang; Houck, Vanessa; Benboubker, Lotfi; Hulin, Cyrille

2018-01-18

This FIRST trial final analysis examined survival outcomes in patients with transplant-ineligible newly diagnosed multiple myeloma (NDMM) treated with lenalidomide and low-dose dexamethasone until disease progression (Rd continuous), Rd for 72 weeks (18 cycles; Rd18), or melphalan, prednisone, and thalidomide (MPT; 72 weeks). The primary endpoint was progression-free survival (PFS; primary comparison: Rd continuous vs MPT). Overall survival (OS) was a key secondary endpoint (final analysis prespecified ≥60 months' follow-up). Patients were randomized to Rd continuous (n = 535), Rd18 (n = 541), or MPT (n = 547). At a median follow-up of 67 months, PFS was significantly longer with Rd continuous vs MPT (hazard ratio [HR], 0.69; 95% confidence interval [CI], 0.59-0.79; P < .00001) and was similarly extended vs Rd18. Median OS was 10 months longer with Rd continuous vs MPT (59.1 vs 49.1 months; HR, 0.78; 95% CI, 0.67-0.92; P = .0023), and similar with Rd18 (62.3 months). In patients achieving complete or very good partial responses, Rd continuous had an ≈30-month longer median time to next treatment vs Rd18 (69.5 vs 39.9 months). Over half of all patients who received second-line treatment were given a bortezomib-based therapy. Second-line outcomes were improved in patients receiving bortezomib after Rd continuous and Rd18 vs after MPT. No new safety concerns, including risk for secondary malignancies, were observed. Treatment with Rd continuous significantly improved survival outcomes vs MPT, supporting Rd continuous as a standard of care for patients with transplant-ineligible NDMM. This trial was registered at www.clinicaltrials.gov as #NCT00689936 and EudraCT as 2007-004823-39. © 2018 by The American Society of Hematology.

Multivariate semi-logistic distribution and processes | Umar | Journal ...

African Journals Online (AJOL)

Multivariate semi-logistic distribution is introduced and studied. Some characterizations properties of multivariate semi-logistic distribution are presented. First order autoregressive minification processes and its generalization to kth order autoregressive minification processes with multivariate semi-logistic distribution as ...

Long-term outcome after neoadjuvant radiochemotherapy in locally advanced noninflammatory breast cancer and predictive factors for a pathologic complete remission. Results of a multivariate analysis

International Nuclear Information System (INIS)

Matuschek, C.; Boelke, E.; Roth, S.L.

2012-01-01

An earlier published series of neoadjuvant radiochemotherapy (NRT-CHX) in locally advanced noninflammatory breast cancer (LABC) has now been updated with a follow-up of more than 15 years. Long-term outcome data and predictive factors for pathologic complete response (pCR) were analyzed. Patients and methods: During 1991-1998, 315 LABC patients (cT1-cT4/cN0-N1) were treated with NRT-CHX. Preoperative radiotherapy (RT) consisted of external beam radiation therapy (EBRT) of 50 Gy (5 x 2 Gy/week) to the breast and the supra-/infraclavicular lymph nodes combined with an electron boost in 214 cases afterwards or - in case of breast conservation - a 10-Gy interstitial boost with 192 Ir afterloading before EBRT. Chemotherapy was administered prior to RT in 192 patients, and concomitantly in 113; 10 patients received no chemotherapy. The update of all follow-up ended in November 2011. Age, tumor grade, nodal status, hormone receptor status, simultaneous vs. sequential CHX, and the time interval between end of RT and surgery were examined in multivariate terms with pCR and overall survival as end point. Results: The total pCR rate after neoadjuvant RT-CHX reached 29.2%, with LABC breast conservation becoming possible in 50.8% of cases. In initially node-positive cases (cN+), a complete nodal response (pN0) after NRT-CHX was observed in 56% (89/159). The multivariate analysis revealed that a longer time interval to surgery increased the probability for a pCR (HR 1.17 [95% CI 1.05-1.31], p 2 months) increases the probability of pCR after NRT-CHX. (orig.)

Multivariate Pareto Minification Processes | Umar | Journal of the ...

African Journals Online (AJOL)

Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate exponential (ME) distribution are presented and discussed. The theory of positive dependence is used to show that in many cases, multivariate exponential autoregressive (MEAR) and multivariate autoregressive moving average ...

Models and Inference for Multivariate Spatial Extremes

KAUST Repository

Vettori, Sabrina

2017-12-07

The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical

Multivariate wavelet frames

CERN Document Server

Skopina, Maria; Protasov, Vladimir

2016-01-01

This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...

Design of a multivariable controller for a CANDU 600 MWe nuclear power plant using the INA method

International Nuclear Information System (INIS)

Roy, N.; Boisvert, J.; Mensah, S.

1984-04-01

The development of large and complex nuclear and process plants requires high-performance control systems, designed with rigorous multivariable techniques. This work is part of an analytical study demonstrating the real potential of multivariable methods. It covers every step in the design of a multi-variable controller for a Gentilly-2 type CANDU 600 MWe nuclear power plant using the Inverse Nyquist Array (INA) method. First the linear design model and its preliminary modifications are described. The design tools are reviewed and the operations required to achieve open-loop diagonal dominance are thoroughly described. Analysis of the closed-loop system is then performed and a feedback matrix is selected to meet the design specifications. The performance of the controller on the linear model is verified by simulation. Finally, the controller is implemented on the reference non-linear model to assess its overall performance. The results show that the INA method can be used successfully to design controllers for large and complex systems

Racial Variation in Vocational Rehabilitation Outcomes: A Structural Equation Modeling Approach

Science.gov (United States)

Martin, Frank H.

2010-01-01

Numerous studies have indicated racial and ethnic disparities in the vocational rehabilitation (VR) system, including differences in acceptance, services provided, closure types, and employment outcomes. Few of these studies, however, have used advanced multivariate techniques or latent constructs to measure quality of employment outcomes (QEO) or…

Mucosal Perforation During Laparoscopic Heller Myotomy Has No Influence on Final Treatment Outcome.

Science.gov (United States)

Salvador, Renato; Spadotto, Lorenzo; Capovilla, Giovanni; Voltarel, Guerrino; Pesenti, Elisa; Longo, Cristina; Cavallin, Francesco; Nicoletti, Loredana; Ruol, Alberto; Valmasoni, Michele; Merigliano, Stefano; Costantini, Mario

2016-12-01

The aims of the study were (a) to examine the final outcome in patients experiencing accidental mucosal perforation during laparoscopic Heller myotomy with Dor fundoplication (LHD) and (b) to evaluate whether perforation episodes might influence the way in which surgeons subsequently approached the LHD procedure. We studied all consecutive patients that underwent LHD between 1992 and 2015. Patients were divided into two main groups: those who experienced an intraoperative mucosal perforation (group P) and those whose LHD was uneventful (group NP). Two additional groups were compared: group A, which consisted of patients operated by a given surgeon immediately before a perforation episode occurred, and group B, which included those operated immediately afterwards. Eight hundred seventy-five patients underwent LHD; a mucosal perforation was detected in 25 patients (2.9 %), which was found unrelated to patients' symptom's score and age, radiological stage, manometric pattern, or the surgeon's experience. The median postoperative symptom score was similar for the two groups as the failure rate: 92 failures in group NP (10.8 %) and 4 in group P (16 %) (p = 0.34); moreover, symptoms recurred in 2 patients of group A (10 %) and 3 patients of group B (15 %) (p = 0.9). Accidental perforation during LHD is infrequent and impossible to predict on the grounds of preoperative therapy or the surgeon's personal experience. Despite a longer surgical procedure and hospital stay, the outcome of LHD is much the same as for patients undergoing uneventful myotomy. A recent mucosal perforation does not influence the surgeon's subsequent performance.

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

Science.gov (United States)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

Procesoptimerende multivariable regulatorer til kraftværkskedler. Process Optimizing Multivariable Controllers for Powerplant Boilers

DEFF Research Database (Denmark)

Hansen, T.

The purpose of this Ph.D. thesis is twofold: The first purpose is to devise a new method for application of multivariable controllers in boiler control systems in which they act as optional process optimizing extensions to conventional control systems and in such a way that the safety measures...... mentioned, the concept is applicable to new as well as existing plants. The seccond purpose is to suggest specific methods for experimental modelling and multivariable controller design which are possible to use under the conceptual framework, implement them and test them in a boiler application....

Multivariate data analysis

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg

Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

Outcomes and Pharmacoeconomic Analysis of a Home Intravenous Antibiotic Infusion Program in Veterans.

Science.gov (United States)

Ruh, Christine A; Parameswaran, Ganapathi I; Wojciechowski, Amy L; Mergenhagen, Kari A

2015-11-01

The use of outpatient parenteral antibiotic therapy (OPAT) programs has become more frequent because of benefits in costs with equivalent clinical outcomes compared with inpatient care. The purpose of this study was to evaluate the outcomes of our program. A modified pharmacoeconomic analysis was performed to compare costs of our program with hospital or rehabilitation facility care. This was a retrospective chart review of 96 courses of OPAT between April 1, 2011, and July 31, 2013. Clinical failures were defined as readmission or death due to worsening infection or readmission secondary to adverse drug event (ADE) to antibiotic therapy. This does not include those patients readmitted for reasons not associated with OPAT therapy, including comorbidities or elective procedures. Baseline characteristics and program-specific data were analyzed. Statistically significant variables were built into a multivariate logistic regression model to determine predictors of failure. A pharmacoeconomic analysis was performed with the use of billing records. Of the total episodes evaluated, 17 (17.71%) clinically failed therapy, and 79 (82.29%) were considered a success. In the multivariate analysis, number of laboratory draws (P = 0.02) and occurrence of drug reaction were significant in the final model, P = 0.02 and P = 0.001, respectively. The presence an adverse drug reaction increases the odds of failure (OR = 10.10; 95% CI, 2.69-44.90). Compared with inpatient or rehabilitation care, the cost savings was $6,932,552.03 or $2,649,870.68, respectively. In our study, patients tolerated OPAT well, with a low number of failures due to ADE. The clinical outcomes and cost savings of our program indicate that OPAT can be a viable alternative to long-term inpatient antimicrobial therapy. Published by Elsevier Inc.

An Exact Confidence Region in Multivariate Calibration

OpenAIRE

Mathew, Thomas; Kasala, Subramanyam

1994-01-01

In the multivariate calibration problem using a multivariate linear model, an exact confidence region is constructed. It is shown that the region is always nonempty and is invariant under nonsingular transformations.

Correlation between audiovestibular function tests and hearing outcomes in severe to profound sudden sensorineural hearing loss.

Science.gov (United States)

Wang, Chi-Te; Huang, Tsung-Wei; Kuo, Shih-Wei; Cheng, Po-Wen

2009-02-01

This study investigated whether audiovestibular function tests, namely auditory brain stem response (ABR) and vestibular-evoked myogenic potential (VEMP) tests were correlated to hearing outcomes after controlling the effects of other potential confounding factors in severe to profound sudden sensorineural hearing loss (SSHL). Eighty-eight patients with severe to profound SSHL were enrolled in this study. Pretreatment hearing levels, results of audiovestibular function tests, and final hearing outcomes were recorded from retrospective chart reviews. Other factors, including age, gender, delay of treatment, vertigo, diabetes mellitus, and hypertension, were collected as well. Comparative analysis between multiple variables and hearing outcomes was conducted using the cumulative logits model in overall subjects. Further, multivariate analysis of prognostic factors was conducted in the stratified groups of severe (70 dB HL 90 dB HL) SSHL. Multivariate analysis showed that pretreatment hearing levels, presence of vertigo, and results of ABR and VEMP testing were significant outcome predictors in the overall subjects. Stratification analysis demonstrated that both the presence of ABR and VEMP waveforms were significantly correlated with better hearing outcomes in the group of severe SSHL [ABR: adjusted odds ratio (aOR) = 14.7, 95% confidence interval (CI) = 1.78 to 122, p = 0.01; VEMP: aOR = 5.91, 95% CI = 1.18 to 29.5, p = 0.03], whereas the presence of vertigo was the only significant negative prognostic factor in the group of profound SSHL (aOR = 0.24, 95% CI = 0.06 to 0.95, p = 0.04). Other variables, including age, gender, diabetes mellitus, hypertension, and delay of treatment, were not significantly related to hearing outcomes in both groups (p > 0.05). A predictive hearing recovery table with the combined ABR and VEMP results was proposed for the group of severe SSHL. ABR and VEMP tests should be included in the battery of neurootological examinations in

Multivariate rational data fitting

Science.gov (United States)

Cuyt, Annie; Verdonk, Brigitte

1992-12-01

Sections 1 and 2 discuss the advantages of an object-oriented implementation combined with higher floating-point arithmetic, of the algorithms available for multivariate data fitting using rational functions. Section 1 will in particular explain what we mean by "higher arithmetic". Section 2 will concentrate on the concepts of "object orientation". In sections 3 and 4 we shall describe the generality of the data structure that can be dealt with: due to some new results virtually every data set is acceptable right now, with possible coalescence of coordinates or points. In order to solve the multivariate rational interpolation problem the data sets are fed to different algorithms depending on the structure of the interpolation points in then-variate space.

Multivariate missing data in hydrology - Review and applications

Science.gov (United States)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Outcomes of multidrug-resistant tuberculosis treatment with early initiation of antiretroviral therapy for HIV co-infected patients in Lesotho.

Directory of Open Access Journals (Sweden)

Hind Satti

Full Text Available BACKGROUND: Although the importance of concurrent treatment for multidrug-resistant tuberculosis (MDR-TB and HIV co-infection has been increasingly recognized, there have been few studies reporting outcomes of MDR-TB and HIV co-treatment. We report final outcomes of comprehensive, integrated MDR-TB and HIV treatment in Lesotho and examine factors associated with death or treatment failure. METHODS: We reviewed clinical charts of all adult patients who initiated MDR-TB treatment in Lesotho between January 2008 and September 2009. We calculated hazard ratios (HR and used multivariable Cox proportional hazards regression to identify predictors of poor outcomes. RESULTS: Of 134 confirmed MDR-TB patients, 83 (62% were cured or completed treatment, 46 (34% died, 3 (2% transferred, 1 (1% defaulted, and 1 (1% failed treatment. Treatment outcomes did not differ significantly by HIV status. Among the 94 (70% patients with HIV co-infection, 53% were already on antiretroviral therapy (ART before MDR-TB treatment initiation, and 43% started ART a median of 16 days after the start of the MDR-TB regimen. Among HIV co-infected patients who died, those who had not started ART before MDR-TB treatment had a shorter median time to death (80 days vs. 138 days, p=0.065. In multivariable analysis, predictors of increased hazard of failure or death were low and severely low body mass index (HR 2.75, 95% confidence interval [CI] 1.27-5.93; HR 5.50, 95% CI 2.38-12.69, and a history of working in South Africa (HR 2.37, 95% CI 1.24-4.52. CONCLUSIONS: Favorable outcomes can be achieved in co-infected patients using a community-based treatment model when both MDR-TB and HIV disease are treated concurrently and treatment is initiated promptly.

Multivariate statistics high-dimensional and large-sample approximations

CERN Document Server

Fujikoshi, Yasunori; Shimizu, Ryoichi

2010-01-01

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

Exploratory multivariate analysis by example using R

CERN Document Server

Husson, Francois; Pages, Jerome

2010-01-01

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin

Revisiting the relationship of three-dimensional fluid attenuation inversion recovery imaging and hearing outcomes in adults with idiopathic unilateral sudden sensorineural hearing loss

Energy Technology Data Exchange (ETDEWEB)

Liao, Wen-Huei [School of Medicine, National Yang Ming University, Taipei, 11221, Taiwan (China); Department of Otolaryngology, Taipei Veterans General Hospital, Taipei, 11217, Taiwan (China); Wu, Hsiu-Mei [School of Medicine, National Yang Ming University, Taipei, 11221, Taiwan (China); Department of Radiology, Taipei Veterans General Hospital, Taipei, 11217, Taiwan (China); Wu, Hung-Yi [Department of Radiology, Taipei Veterans General Hospital, Taipei, 11217, Taiwan (China); Tu, Tzong-Yang; Shiao, An-Suey [School of Medicine, National Yang Ming University, Taipei, 11221, Taiwan (China); Department of Otolaryngology, Taipei Veterans General Hospital, Taipei, 11217, Taiwan (China); Castillo, Mauricio [Department of Radiology, University of North Carolina, Chapel Hill, NC, 27599-7510 (United States); Hung, Sheng-Che, E-mail: hsz829@gmail.com [School of Medicine, National Yang Ming University, Taipei, 11221, Taiwan (China); Department of Radiology, Taipei Veterans General Hospital, Taipei, 11217, Taiwan (China); Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei, 11221, Taiwan (China)

2016-12-15

Background and purpose: Three-dimensional fluid attenuation inversion recovery (3D FLAIR) may demonstrate high signal in the inner ears of patients with idiopathic sudden sensorineural hearing loss (ISSNHL), but the correlations of this finding with outcomes are still controversial. Here we compared 4 3D MRI sequences with the outcomes of patients with ISSNHL. Materials and methods: 77 adult patients with ISSNHL underwent MRI with pre contrast FLAIR, fast imaging employing steady-state acquisition images (FIESTA-C), post contrast T1WI and post contrast FLAIR. The extent and degree of high signal in both cochleas were evaluated in all patients, and asymmetry ratios between the affected ears and the normal ones were calculated. The relationships among MRI findings, including extent and asymmetry of abnormal cochlear high signals, degree of FLAIR enhancement, and clinical information, including age, vestibular symptoms, baseline hearing loss, and final hearing outcomes were analyzed. Results: 54 patients (28 men; age, 52.1 ± 15.5 years) were included in our study. Asymmetric cochlear signal intensities were more frequently observed in pre contrast and post contrast FLAIR (79.6% and 68.5%) than in FIESTA-C (61.1%) and T1WI (51.9%) (p < 0.001). Age, baseline hearing loss, extent of high signal and asymmetry ratios of pre contrast and post contrast FLAIR were all correlated with final hearing outcomes. In multivariate analysis, age and the extent of high signals were the most significant predictors of final hearing outcomes. Conclusion: 3D FLAIR provides a higher sensitivity in detecting the asymmetric cochlear signal abnormality. The more asymmetric FLAIR signals and presence of high signals beyond cochlea indicated a poorer prognosis.

Ellipsoidal prediction regions for multivariate uncertainty characterization

DEFF Research Database (Denmark)

Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul

2018-01-01

, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predefined...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspecification of ellipsoidal prediction regions...

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss in children.

Science.gov (United States)

Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan

2015-09-01

To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Single-incision laparoscopic surgery for locally advanced colorectal cancer : feasibility, short-term and oncologic outcomes.

Science.gov (United States)

Famiglietti, F; Leonard, D; Bachmann, R; Remue, C; Abbes Orabi, N; van Maanen, A; van den Eynde, M; Kartheuser, A

2018-01-01

Data about single-incision laparoscopic surgery (SILS) in locally advanced colorectal cancers are scarce. This study aimed to evaluate perioperative and shortterm oncologic outcomes of SILS in pT3-T4 colorectal cancer. From 2011 to 2015 data from 249 SILS performed in our Colorectal Unit were entered into a prospective database. Data regarding patients with a pT3-T4 colorectal adenocarcinoma were compared to those with pTis-pT2. Factors influencing conversion were assessed by multivariate analysis. There were 100 consecutive patients (T3-T4 = 70, Tis-T2 = 30). Demographics were similar. Tumor size was significantly larger in the T3-T4 group [3.9cm vs 2cm; p2) postoperative complication rate was similar between groups (8.6% vs 10% ; p = 0.999), as well as conversion rate (18.6% vs 6.7% ; p = 0.220). Finally, there were no differences in terms of hospital stay and mortality rate. On multivariate analysis, age (OR = 1.06, 95%CI: 1.012-1.113 ; p = 0.015] and stage IV (OR = 5.372, 95%CI: 1.320-21.862, p = 0.019) were independently associated with conversion. SILS for locally advanced colorectal cancer did not affect the short-term outcomes in this series and oncological clearance remained satisfactory. Age and stage IV disease are independent risk factors for conversion. © Acta Gastro-Enterologica Belgica.

Control Multivariable por Desacoplo

Directory of Open Access Journals (Sweden)

Fernando Morilla

2013-01-01

Full Text Available Resumen: La interacción entre variables es una característica inherente de los procesos multivariables, que dificulta su operación y el diseño de sus sistemas de control. Bajo el paradigma de Control por desacoplo se agrupan un conjunto de metodologías, que tradicionalmente han estado orientadas a eliminar o reducir la interacción, y que recientemente algunos investigadores han reorientado con objetivos de solucionar un problema tan complejo como es el control multivariable. Parte del material descrito en este artículo es bien conocido en el campo del control de procesos, pero la mayor parte de él son resultados de varios años de investigación de los autores en los que han primado la generalización del problema, la búsqueda de soluciones de fácil implementación y la combinación de bloques elementales de control PID. Esta conjunción de intereses provoca que no siempre se pueda conseguir un desacoplo perfecto, pero que sí se pueda conseguir una considerable reducción de la interacción en el nivel básico de la pirámide de control, en beneficio de otros sistemas de control que ocupan niveles jerárquicos superiores. El artículo resume todos los aspectos básicos del Control por desacoplo y su aplicación a dos procesos representativos: una planta experimental de cuatro tanques acoplados y un modelo 4×4 de un sistema experimental de calefacción, ventilación y aire acondicionado. Abstract: The interaction between variables is inherent in multivariable processes and this fact may complicate their operation and control system design. Under the paradigm of decoupling control, several methodologies that traditionally have been addressed to cancel or reduce the interactions are gathered. Recently, this approach has been reoriented by several researchers with the aim to solve such a complex problem as the multivariable control. Parts of the material in this work are well known in the process control field; however, most of them are

Multivariate Time Series Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

Intelligent multivariate process supervision

International Nuclear Information System (INIS)

Visuri, Pertti.

1986-01-01

This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

Graphics for the multivariate two-sample problem

International Nuclear Information System (INIS)

Friedman, J.H.; Rafsky, L.C.

1981-01-01

Some graphical methods for comparing multivariate samples are presented. These methods are based on minimal spanning tree techniques developed for multivariate two-sample tests. The utility of these methods is illustrated through examples using both real and artificial data

Can we predict final outcome of internal medicine residents with in-training evaluation.

Science.gov (United States)

Chierakul, Nitipatana; Pongprasobchai, Supot; Boonyapisit, Kanokwan; Chinthammitr, Yingyong; Pithukpakorn, Manop; Maneesai, Adisak; Srivijitkamol, Apiradee; Koomanachai, Pornpan; Koolvisoot, Ajchara; Tanwandee, Tawesak; Shayakul, Chairat; Kachintorn, Udom

2011-02-01

To assess the predictive value of in-training evaluation for determining future success in the internal medicine board certifying examination. Ninety-seven internal medicine residents from Faculty of Medicine Siriraj Hospital who undertake the Thai Board examination during the academic year 2006-2008 were enrolled. Correlation between the scores during internal medicine rotation and final scores in board examination were then examined. Significant positive linear correlation was found between scores from both written and clinical parts of board certifying examination and scores from the first-year summative written and clinical examinations and also the second-year formative written examination (r = 0.43-0.68, p evaluation by attending staffs was less well correlated (r = 0.29-0.36) and the evaluation by nurses or medical students demonstrated inverse relationship (r = -0.2, p = 0.27 and r = -0.13, p = 0.48). Some methods of in-training evaluation can predict successful outcome of board certifying examination. Multisource assessments cannot well extrapolate some aspects of professional competences and qualities.

Multivariable biorthogonal continuous--discrete Wilson and Racah polynomials

International Nuclear Information System (INIS)

Tratnik, M.V.

1990-01-01

Several families of multivariable, biorthogonal, partly continuous and partly discrete, Wilson polynomials are presented. These yield limit cases that are purely continuous in some of the variables and purely discrete in the others, or purely discrete in all the variables. The latter are referred to as the multivariable biorthogonal Racah polynomials. Interesting further limit cases include the multivariable biorthogonal Hahn and dual Hahn polynomials

Calculus of multivariate functions: it's application in business | Awen ...

African Journals Online (AJOL)

Multivariate functions can be applied to situations in business organizations like ... of capital invested in the plant, the size of the labour force and the cost of raw ... of multivariate functions and has considered types of multivariate differentiation ...

Multivariate calculus and geometry

CERN Document Server

Dineen, Seán

2014-01-01

Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.

A Multivariate Approach to Dilepton Analyses in the Upgraded ALICE Detector at CERN-LHC

CERN Document Server

AUTHOR|(CDS)2242451; Weber, Michael

ALICE, the dedicated heavy-ion experiment at CERN-LHC, will undergo a major upgrade in 2019/20. This work aims to assess the feasibility of conventional and multivariate analysis techniques for low-mass dielectron measurements in Pb-Pb collisions in a scenario involving the upgraded ALICE detector with a low magnetic field ($B=0.2~\\text{T}$). These electron-positron pairs are promising probes for the hot and dense medium, which is created in collisions of ultra-relativistic heavy nuclei, as they traverse the medium without significant final-state modifications. Due to their small signal-to-background ratio, high-purity dielectron samples are required. They can be provided by conventional analysis methods, which are based on sequential cuts, however at the price of low signal efficiency. This work shows that existing methods can be improved by employing multivariate approaches to reject different background sources of the dielectron invariant mass spectrum. The major background components are dielectrons from ...

Factors related to positive and negative outcomes in psychiatric inpatients in a General Hospital Psychiatric Unit: a proposal for an outcomes index

Directory of Open Access Journals (Sweden)

HUGO KARLING MORESCHI

2015-02-01

Full Text Available Background General Hospital Psychiatric Units have a fundamental importance in the mental health care systems. However, there is a lack of studies regarding the level of improvement of patients in this type of facility. Objective To assess factors related to good and poor outcomes in psychiatric inpatients using an index composed by clinical parameters easily measured. Methods Length of stay (LOS, Global Assessment of Functioning (variation and at discharge and Clinical Global Impression (severity and improvement were used to build a ten-point improvement index (I-Index. Records of psychiatric inpatients of a general hospital during an 18-month period were analyzed. Three groups (poor, intermediate and good outcomes were compared by univariate and multivariate models according to clinical and sociodemographic variables. Results Two hundred and fifty patients were included, with a percentage in the groups with poor, regular and good outcomes of 16.4%, 59,6% and 24.0% respectively. Poor outcome at the discharge was associated mainly with lower education, transient disability, antipsychotics use, chief complaint “behavioral change/aggressiveness” and psychotic features. Multivariate analysis found a higher OR for diagnoses of “psychotic disorders” and “personality disorders” and others variables in relation to protective categories in the poor outcome group compared to the good outcome group. Discussion Our I-Index proved to be an indicator of that allows an easy and more comprehensive evaluation to assess outcomes of inpatients than just LOS. Different interventions addressed to conditions such as psychotic disorders and disruptive chief complaints are necessary.

Multivariable Feedback Control of Nuclear Reactors

Directory of Open Access Journals (Sweden)

Rune Moen

1982-07-01

Full Text Available Multivariable feedback control has been adapted for optimal control of the spatial power distribution in nuclear reactor cores. Two design techniques, based on the theory of automatic control, were developed: the State Variable Feedback (SVF is an application of the linear optimal control theory, and the Multivariable Frequency Response (MFR is based on a generalization of the traditional frequency response approach to control system design.

Multivariate Marshall and Olkin Exponential Minification Process ...

African Journals Online (AJOL)

A stationary bivariate minification process with bivariate Marshall-Olkin exponential distribution that was earlier studied by Miroslav et al [15]is in this paper extended to multivariate minification process with multivariate Marshall and Olkin exponential distribution as its stationary marginal distribution. The innovation and the ...

Matrix-based introduction to multivariate data analysis

CERN Document Server

Adachi, Kohei

2016-01-01

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...

Consultant input in acute medical admissions and patient outcomes in hospitals in England: a multivariate analysis.

Science.gov (United States)

Bell, Derek; Lambourne, Adrian; Percival, Frances; Laverty, Anthony A; Ward, David K

2013-01-01

Recent recommendations for physicians in the UK outline key aspects of care that should improve patient outcomes and experience in acute hospital care. Included in these recommendations are Consultant patterns of work to improve timeliness of clinical review and improve continuity of care. This study used a contemporaneous validated survey compared with clinical outcomes derived from Hospital Episode Statistics, between April 2009 and March 2010 from 91 acute hospital sites in England to evaluate systems of consultant cover for acute medical admissions. Clinical outcomes studied included adjusted case fatality rates (aCFR), including the ratio of weekend to weekday mortality, length of stay and readmission rates. Hospitals that had an admitting Consultant presence within the Acute Medicine Unit (AMU, or equivalent) for a minimum of 4 hours per day (65% of study group) had a lower aCFR compared with hospitals that had Consultant presence for less than 4 hours per day (p40 acute medical admissions per day had a lower aCFR compared to hospitals with fewer than 40 admissions per day (pstudy is the first large study to explore the potential relationships between systems of providing acute medical care and clinical outcomes. The results show an association between well-designed systems of Consultant working practices, which promote increased patient contact, and improved patient outcomes in the acute hospital setting.

Leukoaraiosis predicts poor 90-day outcome after acute large cerebral artery occlusion.

Science.gov (United States)

Henninger, Nils; Lin, Eugene; Baker, Stephen P; Wakhloo, Ajay K; Takhtani, Deepak; Moonis, Majaz

2012-01-01

To date limited information regarding outcome-modifying factors in patients with acute intracranial large artery occlusion (ILAO) in the anterior circulation is available. Leukoaraiosis (LA) is a common finding among patients with ischemic stroke and has been associated with poor post-stroke outcomes but its association with ILAO remains poorly characterized. This study sought to clarify the contribution of baseline LA and other common risk factors to 90-day outcome (modified Rankin Scale, mRS) after stroke due to acute anterior circulation ILAO. We retrospectively analyzed 1,153 consecutive patients with imaging-confirmed ischemic stroke during a 4-year period (2007-2010) at a single academic institution. The final study cohort included 87 patients with acute ILAO subjected to multimodal CT imaging within 24 h of symptom onset. LA severity was assessed using the van Swieten scale on non-contrast CT. Leptomeningeal collaterals were graded using CT angiogram source images. Hemorrhagic transformation (HT) was determined on follow-up CT. Multivariate logistic regression controlling for HT, treatment modality, demographic, as well as baseline clinical and imaging characteristics was used to identify independent predictors of a poor outcome (90-day mRS >2). The median National Institutes of Health Stroke Scale (NIHSS) at baseline was 15 (interquartile range 9-21). Twenty-four percent of the studied patients had severe LA. They were more likely to have hypertension (p = 0.028), coronary artery disease (p = 0.015), poor collaterals (p Coexisting LA may predict poor functional outcome in patients with acute anterior circulation ILAO independent of other known important outcome predictors such as comorbid state, admission functional deficit, collateral status, hemorrhagic conversion, and treatment modality. Copyright © 2012 S. Karger AG, Basel.

Marshall-Olkin multivariate semi-logistic distribution and minification ...

African Journals Online (AJOL)

Olkin multivariate logistic distribution (MO-ML) are introduced and studied. Various characterizations properties of Marshall-Olkin multivariate semi-logistic distribution are investigated and studied. First order autoregressive minification processes ...

Multivariate Cryptography Based on Clipped Hopfield Neural Network.

Science.gov (United States)

Wang, Jia; Cheng, Lee-Ming; Su, Tong

2018-02-01

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

Consultant input in acute medical admissions and patient outcomes in hospitals in England: a multivariate analysis.

Directory of Open Access Journals (Sweden)

Derek Bell

Full Text Available Recent recommendations for physicians in the UK outline key aspects of care that should improve patient outcomes and experience in acute hospital care. Included in these recommendations are Consultant patterns of work to improve timeliness of clinical review and improve continuity of care. This study used a contemporaneous validated survey compared with clinical outcomes derived from Hospital Episode Statistics, between April 2009 and March 2010 from 91 acute hospital sites in England to evaluate systems of consultant cover for acute medical admissions. Clinical outcomes studied included adjusted case fatality rates (aCFR, including the ratio of weekend to weekday mortality, length of stay and readmission rates. Hospitals that had an admitting Consultant presence within the Acute Medicine Unit (AMU, or equivalent for a minimum of 4 hours per day (65% of study group had a lower aCFR compared with hospitals that had Consultant presence for less than 4 hours per day (p40 acute medical admissions per day had a lower aCFR compared to hospitals with fewer than 40 admissions per day (p<0.03 and had a lower 7 day re-admission rate (p<0.02. This study is the first large study to explore the potential relationships between systems of providing acute medical care and clinical outcomes. The results show an association between well-designed systems of Consultant working practices, which promote increased patient contact, and improved patient outcomes in the acute hospital setting.

Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

Science.gov (United States)

Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

2013-06-01

Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

Functional outcomes in patients with chronic obstructive pulmonary disease: a multivariate analysis

Directory of Open Access Journals (Sweden)

Filipe T. S. Athayde

2014-01-01

Full Text Available Background: Multiple factors can influence the severity of chronic obstructive pulmonary disease (COPD and the functioning of patients with COPD, such as personal characteristics and systemic manifestations. Objective: To evaluate the different factors that can influence the activity and psychosocial impact domains of the Saint George's Respiratory Questionnaire (SGRQ in COPD patients. Method: Participants, recruited in a university-based hospital, responded to the SGRQ, and in addition, personal, anthropometric, and clinical data were collected. The study was approved by the Institutional Ethics Committee. Data were analyzed using multiple linear regression models, with the SGRQ activity and psychosocial impact scores as outcome variables, and 10 explanatory variables (age, gender, forced expiratory volume in the first second - FEV1, smoking load, body mass index, oxygen therapy, associated diseases, regular physical activity, participation in a formal rehabilitation program, and SGRQ symptoms score were considered. Results: The best regression model for predicting the SGRQ activity score (r2=0.477 included gender, FEV1, and SGRQ symptoms. In contrast, the predictive model with the highest proportion of explained variance in psychosocial impact score (r2=0.426 included the variables gender, oxygen therapy, and SGRQ symptoms. Conclusions: The results indicate that the outcomes, while based on functioning parameters in COPD patients, could be partly explained by the personal and clinical factors analyzed, especially by the symptoms assessed by the SGRQ. Thus, it appears that the health conditions of these patients cannot be described by isolated variables, including pulmonary function parameters.

A MATLAB companion for multivariable calculus

CERN Document Server

Cooper, Jeffery

2001-01-01

Offering a concise collection of MatLab programs and exercises to accompany a third semester course in multivariable calculus, A MatLab Companion for Multivariable Calculus introduces simple numerical procedures such as numerical differentiation, numerical integration and Newton''s method in several variables, thereby allowing students to tackle realistic problems. The many examples show students how to use MatLab effectively and easily in many contexts. Numerous exercises in mathematics and applications areas are presented, graded from routine to more demanding projects requiring some programming. Matlab M-files are provided on the Harcourt/Academic Press web site at http://www.harcourt-ap.com/matlab.html.* Computer-oriented material that complements the essential topics in multivariable calculus* Main ideas presented with examples of computations and graphics displays using MATLAB * Numerous examples of short code in the text, which can be modified for use with the exercises* MATLAB files are used to implem...

Comparison of surgical and non-surgical orthodontic treatment approaches on occlusal and cephalometric outcomes in patients with Class II Division I malocclusions

Directory of Open Access Journals (Sweden)

Sheila Daniels

2017-07-01

Full Text Available Abstract Background This study aimed to examine end-of-treatment outcomes of severe Class II Division I malocclusion patients treated with surgical or non-surgical approaches. This study tests the hypotheses that occlusal outcomes (ABO-OGS and cephalometric outcomes differ between these groups. Methods A total of 60 patients were included: 20 of which underwent surgical correction and 40 of which did not. Cast grading of initial and final study models was performed and information was gathered from pre- to post-treatment cephalometric radiographs. The end-of-treatment ABO-OGS and cephalometric outcomes were compared to Mann-Whitney U tests and multivariable linear regression models. Results Following adjustment for multiple confounders (age, gender, complexity of case, and skeletal patterns, the final deband score (ABO-OGS was similar for both groups (23.8 for surgical group versus 22.5 for non-surgical group. Those treated surgically had a significantly larger reduction in ANB angle, 3.4° reduction versus 1.5° reduction in the non-surgical group (p = 0.002. The surgical group also showed increased maxillary incisor proclination (p = 0.001 compared to the non-surgical group. This might be attributed to retroclination of maxillary incisors during treatment selection in the non-surgical group—namely, extraction of premolars to mask the discrepancy. Conclusions Those treated surgically had a significantly larger reduction in ANB angle and increased maxillary incisor proclination compared to those treated non-surgically with no significant changes in occlusal outcomes.

Decoding the complex brain: multivariate and multimodal analyses of neuroimaging data

International Nuclear Information System (INIS)

Salami, Alireza

2012-01-01

various cognitive questions. These methods are used in order to extract features that are inaccessible using univariate / unimodal analytic approaches. To this end, I implemented multivariate partial least squares analysis in study I and II in order to identify neural commonalities and differences between the available and accessible information in memory (study I), and also between episodic encoding and episodic retrieval (study II). Study I provided evidence of a qualitative differences between availability and accessibility signals in memory by linking memory access to modality-independent brain regions, and availability in memory to elevated activity in modality-specific brain regions. Study II provided evidence in support of general and specific memory operations during encoding and retrieval by linking general processes to the joint demands on attentional, executive, and strategic processing, and a process-specific network to core episodic memory function. In study II, III, and IV, I explored whether the age-related changes/differences in one modality were driven by age-related changes/differences in another modality. To this end, study II investigated whether age-related functional differences in hippocampus during an episodic memory task could be accounted for by age-related structural differences. I found that age-related local structural deterioration could partially but not entirely account for age-related diminished hippocampal activation. In study III, I sought to explore whether age-related changes in the prefrontal and occipital cortex during a semantic memory task were driven by local and/or distal gray matter loss. I found that age-related diminished prefrontal activation was driven, at least in part, by local gray matter atrophy, whereas the age-related decline in occipital cortex was accounted for by distal gray matter atrophy. Finally, in study IV, I investigated whether white matter (WM) microstructural differences mediated age-related decline in

Decoding the complex brain: multivariate and multimodal analyses of neuroimaging data

Energy Technology Data Exchange (ETDEWEB)

Salami, Alireza

2012-07-01

various cognitive questions. These methods are used in order to extract features that are inaccessible using univariate / unimodal analytic approaches. To this end, I implemented multivariate partial least squares analysis in study I and II in order to identify neural commonalities and differences between the available and accessible information in memory (study I), and also between episodic encoding and episodic retrieval (study II). Study I provided evidence of a qualitative differences between availability and accessibility signals in memory by linking memory access to modality-independent brain regions, and availability in memory to elevated activity in modality-specific brain regions. Study II provided evidence in support of general and specific memory operations during encoding and retrieval by linking general processes to the joint demands on attentional, executive, and strategic processing, and a process-specific network to core episodic memory function. In study II, III, and IV, I explored whether the age-related changes/differences in one modality were driven by age-related changes/differences in another modality. To this end, study II investigated whether age-related functional differences in hippocampus during an episodic memory task could be accounted for by age-related structural differences. I found that age-related local structural deterioration could partially but not entirely account for age-related diminished hippocampal activation. In study III, I sought to explore whether age-related changes in the prefrontal and occipital cortex during a semantic memory task were driven by local and/or distal gray matter loss. I found that age-related diminished prefrontal activation was driven, at least in part, by local gray matter atrophy, whereas the age-related decline in occipital cortex was accounted for by distal gray matter atrophy. Finally, in study IV, I investigated whether white matter (WM) microstructural differences mediated age-related decline in

An optimal multivariable controller for transcritical CO2 refrigeration cycle with an adjustable ejector

International Nuclear Information System (INIS)

He, Yang; Deng, Jianqiang; Yang, Fusheng; Zhang, Zaoxiao

2017-01-01

Highlights: • Dynamic model for transcritical CO 2 ejector refrigeration system is developed. • A model-driven optimal multivariable controller is proposed. • Gas cooler pressure and cooling capacity are tracked independently. • Maximal performance for a given load is achieved by the optimal controller. - Abstract: The fixed ejector has to work under a restricted operating condition to keep its positive effectiveness on the transcritical CO 2 refrigeration cycle, and a controllable ejector will be helpful. In this paper, an optimal multivariable controller based on the dynamic model is proposed to improve transcritical CO 2 refrigeration cycle with an adjustable ejector (TCRAE). A nonlinear dynamic model is first developed to model the dynamic characteristic of TCRAE. The corresponding model linearization is carried out and the simulation results reproduce transient behavior of the nonlinear model very well. Based on the developed model, an optimal multivariable controller with a tracker based linear quadratic state feedback algorithm and a predictor using steepest descent method is designed. The controller is finally applied on the experimental apparatus and the performance is verified. Using the tracker only, the gas cooler pressure and chilled water outlet temperature (cooling capacity) are well tracked rejecting the disturbances from each other. Furthermore, by the predictor, the optimal gas cooler pressure for a constant cooling capacity is actually approached on the experimental apparatus with a settling time about 700 s.

Multivariate Time Series Decomposition into Oscillation Components.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Acute Thoracolumbar Spinal Cord Injury: Relationship of Cord Compression to Neurological Outcome.

Science.gov (United States)

Skeers, Peta; Battistuzzo, Camila R; Clark, Jillian M; Bernard, Stephen; Freeman, Brian J C; Batchelor, Peter E

2018-02-21

Spinal cord injury in the cervical spine is commonly accompanied by cord compression and urgent surgical decompression may improve neurological recovery. However, the extent of spinal cord compression and its relationship to neurological recovery following traumatic thoracolumbar spinal cord injury is unclear. The purpose of this study was to quantify maximum cord compression following thoracolumbar spinal cord injury and to assess the relationship among cord compression, cord swelling, and eventual clinical outcome. The medical records of patients who were 15 to 70 years of age, were admitted with a traumatic thoracolumbar spinal cord injury (T1 to L1), and underwent a spinal surgical procedure were examined. Patients with penetrating injuries and multitrauma were excluded. Maximal osseous canal compromise and maximal spinal cord compression were measured on preoperative mid-sagittal computed tomography (CT) scans and T2-weighted magnetic resonance imaging (MRI) by observers blinded to patient outcome. The American Spinal Injury Association (ASIA) Impairment Scale (AIS) grades from acute hospital admission (≤24 hours of injury) and rehabilitation discharge were used to measure clinical outcome. Relationships among spinal cord compression, canal compromise, and initial and final AIS grades were assessed via univariate and multivariate analyses. Fifty-three patients with thoracolumbar spinal cord injury were included in this study. The overall mean maximal spinal cord compression (and standard deviation) was 40% ± 21%. There was a significant relationship between median spinal cord compression and final AIS grade, with grade-A patients (complete injury) exhibiting greater compression than grade-C and D patients (incomplete injury) (p compression as independently influencing the likelihood of complete spinal cord injury (p compression. Greater cord compression is associated with an increased likelihood of severe neurological deficits (complete injury) following

Radiation Therapy Noncompliance and Clinical Outcomes in an Urban Academic Cancer Center

Energy Technology Data Exchange (ETDEWEB)

Ohri, Nitin [Department of Radiation Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York (United States); Rapkin, Bruce D. [Department of Epidemiology and Population Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York (United States); Guha, Chandan; Kalnicki, Shalom [Department of Radiation Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York (United States); Garg, Madhur, E-mail: mgarg@montefiore.org [Department of Radiation Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York (United States)

2016-06-01

Purpose: To examine associations between radiation therapy (RT) noncompliance and clinical outcomes. Methods and Materials: We reviewed all patients who completed courses of external beam RT with curative intent in our department from the years 2007 to 2012 for cancers of the head and neck, breast, lung, cervix, uterus, or rectum. Patients who missed 2 or more scheduled RT appointments (excluding planned treatment breaks) were deemed noncompliant. Univariate, multivariable, and propensity-matched analyses were performed to examine associations between RT noncompliance and clinical outcomes. Results: Of 1227 patients, 266 (21.7%) were noncompliant. With median follow-up of 50.9 months, 108 recurrences (8.8%) and 228 deaths (18.6%) occurred. In univariate analyses, RT noncompliance was associated with increased recurrence risk (5-year cumulative incidence 16% vs 7%, P<.001), inferior recurrence-free survival (5-year actuarial rate 63% vs 79%, P<.001), and inferior overall survival (5-year actuarial rate 72% vs 83%, P<.001). In multivariable analyses that were adjusted for disease site and stage, comorbidity score, gender, ethnicity, race, and socioeconomic status (SES), RT noncompliance was associated with inferior recurrence, recurrence-free survival, and overall survival rates. Propensity score–matched models yielded results nearly identical to those seen in univariate analyses. Low SES was associated with RT noncompliance and was associated with inferior clinical outcomes in univariate analyses, but SES was not associated with inferior outcomes in multivariable models. Conclusion: For cancer patients being treated with curative intent, RT noncompliance is associated with inferior clinical outcomes. The magnitudes of these effects demonstrate that RT noncompliance can serve as a behavioral biomarker to identify high-risk patients who require additional interventions. Treatment compliance may mediate the associations that have been observed linking SES and

Multivariate Receptor Models for Spatially Correlated Multipollutant Data

KAUST Repository

Jun, Mikyoung; Park, Eun Sug

2013-01-01

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air

Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data.

Science.gov (United States)

Duan, L L; Szczesniak, R D; Wang, X

2017-11-01

Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization.

Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data

Science.gov (United States)

Duan, L. L.; Szczesniak, R. D.; Wang, X.

2018-01-01

Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization. PMID:29576735

AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

OpenAIRE

Krzyśko, Mirosław; Smaga, Łukasz

2017-01-01

In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...

The influence of television and video game use on attention and school problems: a multivariate analysis with other risk factors controlled.

Science.gov (United States)

Ferguson, Christopher J

2011-06-01

Research on youth mental health has increasingly indicated the importance of multivariate analyses of multiple risk factors for negative outcomes. Television and video game use have often been posited as potential contributors to attention problems, but previous studies have not always been well-controlled or used well-validated outcome measures. The current study examines the multivariate nature of risk factors for attention problems symptomatic of attention deficit hyperactivity disorder and poor school performance. A predominantly Hispanic population of 603 children (ages 10-14) and their parents/guardians responded to multiple behavioral measures. Outcome measures included parent and child reported attention problem behaviors on the Child Behavior Checklist (CBCL) as well as poor school performance as measured by grade point average (GPA). Results found that internal factors such as male gender, antisocial traits, family environment and anxiety best predicted attention problems. School performance was best predicted by family income. Television and video game use, whether total time spent using, or exposure to violent content specifically, did not predict attention problems or GPA. Television and video game use do not appear to be significant predictors of childhood attention problems. Intervention and prevention efforts may be better spent on other risk factors. Copyright © 2010 Elsevier Ltd. All rights reserved.

Can bone scintigraphy predict the final outcome of pasteurized autografts?

International Nuclear Information System (INIS)

Eid, Ahmed Shawky; Jeon, Dae-Geun; Cho, Wan Hyeong

2010-01-01

As pasteurization is becoming more widely used in limb salvage reconstruction, more study is required to understand about host-graft junction healing, graft revascularization and incorporation, and the incidence and type of complications among pasteurized autografts. This was mainly achieved by follow-up radiography. We aimed to clarify whether Tc99m bone scanning can be considered a reliable method in determining these three parameters. Twenty-seven osteosarcoma patients with pasteurized autograft reconstructions were retrospectively reviewed using available scintigraphic and radiographic follow-up every 6 months postoperatively for 36 months. Follow-up of the unhealed cases was continued for the maximum follow-up period available for each case beyond the original study period, ranging from 1 to 15 months. Tc99m uptake was classified as cold, faint, moderate and high uptake. Junction healing was classified as none, partial and complete healing. Seventy percent of junctions united with a mean of 22 months. Ninety to 100% of junctions showed increased uptake (high or moderate) at one time of the study regardless of final outcome. 85% of the pasteurized grafts showed the characteristic ''tramline appearance''. Four grafts (15%) were complicated: pseudoarthrosis and implant failure (1), fractured plate (1), intramedullary nail (IMN) fracture (1), and prosthesis stem loosening in the host bone (1), with underlying unhealed junctions in all cases. Bone scanning can determine the stages of the graft's rim revascularization and incorporation; however, it cannot detect or predict junction healing or occurrence of complications. Supplementary treatment of unhealed junctions showing either decreased junctional uptake or graft quiescence may be warranted. Otherwise, detection of distant metastasis and early local recurrence remains the main application of Tc99m scanning in the management of bone sarcomas. (orig.)

Can bone scintigraphy predict the final outcome of pasteurized autografts?

Energy Technology Data Exchange (ETDEWEB)

Eid, Ahmed Shawky [Ain Shams University, Department of Orthopedic Surgery, Cairo (Egypt); Jeon, Dae-Geun; Cho, Wan Hyeong [Korea Cancer Center Hospital, Department of Orthopedic Surgery, Seoul (Korea)

2010-10-15

As pasteurization is becoming more widely used in limb salvage reconstruction, more study is required to understand about host-graft junction healing, graft revascularization and incorporation, and the incidence and type of complications among pasteurized autografts. This was mainly achieved by follow-up radiography. We aimed to clarify whether Tc99m bone scanning can be considered a reliable method in determining these three parameters. Twenty-seven osteosarcoma patients with pasteurized autograft reconstructions were retrospectively reviewed using available scintigraphic and radiographic follow-up every 6 months postoperatively for 36 months. Follow-up of the unhealed cases was continued for the maximum follow-up period available for each case beyond the original study period, ranging from 1 to 15 months. Tc99m uptake was classified as cold, faint, moderate and high uptake. Junction healing was classified as none, partial and complete healing. Seventy percent of junctions united with a mean of 22 months. Ninety to 100% of junctions showed increased uptake (high or moderate) at one time of the study regardless of final outcome. 85% of the pasteurized grafts showed the characteristic ''tramline appearance''. Four grafts (15%) were complicated: pseudoarthrosis and implant failure (1), fractured plate (1), intramedullary nail (IMN) fracture (1), and prosthesis stem loosening in the host bone (1), with underlying unhealed junctions in all cases. Bone scanning can determine the stages of the graft's rim revascularization and incorporation; however, it cannot detect or predict junction healing or occurrence of complications. Supplementary treatment of unhealed junctions showing either decreased junctional uptake or graft quiescence may be warranted. Otherwise, detection of distant metastasis and early local recurrence remains the main application of Tc99m scanning in the management of bone sarcomas. (orig.)

Alternating multivariate trigonometric functions and corresponding Fourier transforms

International Nuclear Information System (INIS)

Klimyk, A U; Patera, J

2008-01-01

We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group A n , which is a subgroup of the permutation (symmetric) group S n . These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel

Fractional and multivariable calculus model building and optimization problems

CERN Document Server

Mathai, A M

2017-01-01

This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...

A MULTIVARIATE WEIBULL DISTRIBUTION

Directory of Open Access Journals (Sweden)

Cheng Lee

2010-07-01

Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.

Multivariate max-stable spatial processes

KAUST Repository

Genton, Marc G.; Padoan, S. A.; Sang, H.

2015-01-01

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

Multivariate max-stable spatial processes

KAUST Repository

Genton, Marc G.

2015-02-11

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

A comparison of multivariate genome-wide association methods

DEFF Research Database (Denmark)

Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M

2014-01-01

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...

Energy consumption and economic growth in New Zealand: Results of trivariate and multivariate models

International Nuclear Information System (INIS)

Bartleet, Matthew; Gounder, Rukmani

2010-01-01

This study examines the energy consumption-growth nexus in New Zealand. Causal linkages between energy and macroeconomic variables are investigated using trivariate demand-side and multivariate production models. Long run and short run relationships are estimated for the period 1960-2004. The estimated results of demand model reveal a long run relationship between energy consumption, real GDP and energy prices. The short run results indicate that real GDP Granger-causes energy consumption without feedback, consistent with the proposition that energy demand is a derived demand. Energy prices are found to be significant for energy consumption outcomes. Production model results indicate a long run relationship between real GDP, energy consumption and employment. The Granger-causality is found from real GDP to energy consumption, providing additional evidence to support the neoclassical proposition that energy consumption in New Zealand is fundamentally driven by economic activities. Inclusion of capital in the multivariate production model shows short run causality from capital to energy consumption. Also, changes in real GDP and employment have significant predictive power for changes in real capital.

Multivariate and multiscale data assimilation in terrestrial systems: a review.

Science.gov (United States)

Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry

2012-11-26

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Directory of Open Access Journals (Sweden)

Harry Vereecken

2012-11-01

Full Text Available More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF, Particle Filter (PF and variational methods (3/4D-VAR. In this review, we distinguish between four major DA approaches: (1 univariate single-scale DA (UVSS, which is the approach used in the majority of published DA applications, (2 univariate multiscale DA (UVMS referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3 multivariate single-scale DA (MVSS dealing with the assimilation of at least two different data types, and (4 combined multivariate multiscale DA (MVMS. Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a

Multivariate methods in nuclear waste remediation: Needs and applications

International Nuclear Information System (INIS)

Pulsipher, B.A.

1992-05-01

The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs

Corporal Punishment and Student Outcomes in Rural Schools

Science.gov (United States)

Han, Seunghee

2014-01-01

This study examined the effects of corporal punishment on student outcomes in rural schools by analyzing 1,067 samples from the School Survey on Crime and Safety 2007-2008. Results of descriptive statistics and multivariate regression analyses indicated that schools with corporal punishment may decrease students' violent behaviors and…

Sensor failure and multivariable control for airbreathing propulsion systems. Ph.D. Thesis - Dec. 1979 Final Report

Science.gov (United States)

Behbehani, K.

1980-01-01

A new sensor/actuator failure analysis technique for turbofan jet engines was developed. Three phases of failure analysis, namely detection, isolation, and accommodation are considered. Failure detection and isolation techniques are developed by utilizing the concept of Generalized Likelihood Ratio (GLR) tests. These techniques are applicable to both time varying and time invariant systems. Three GLR detectors are developed for: (1) hard-over sensor failure; (2) hard-over actuator failure; and (3) brief disturbances in the actuators. The probability distribution of the GLR detectors and the detectability of sensor/actuator failures are established. Failure type is determined by the maximum of the GLR detectors. Failure accommodation is accomplished by extending the Multivariable Nyquest Array (MNA) control design techniques to nonsquare system designs. The performance and effectiveness of the failure analysis technique are studied by applying the technique to a turbofan jet engine, namely the Quiet Clean Short Haul Experimental Engine (QCSEE). Single and multiple sensor/actuator failures in the QCSEE are simulated and analyzed and the effects of model degradation are studied.

Impact of FAB classification on predicting outcome in acute myeloid leukemia, not otherwise specified, patients undergoing allogeneic stem cell transplantation in CR1: An analysis of 1690 patients from the acute leukemia working party of EBMT.

Science.gov (United States)

Canaani, Jonathan; Beohou, Eric; Labopin, Myriam; Socié, Gerard; Huynh, Anne; Volin, Liisa; Cornelissen, Jan; Milpied, Noel; Gedde-Dahl, Tobias; Deconinck, Eric; Fegueux, Nathalie; Blaise, Didier; Mohty, Mohamad; Nagler, Arnon

2017-04-01

The French, American, and British (FAB) classification system for acute myeloid leukemia (AML) is extensively used and is incorporated into the AML, not otherwise specified (NOS) category in the 2016 WHO edition of myeloid neoplasm classification. While recent data proposes that FAB classification does not provide additional prognostic information for patients for whom NPM1 status is available, it is unknown whether FAB still retains a current prognostic role in predicting outcome of AML patients undergoing allogeneic stem cell transplantation. Using the European Society of Blood and Bone Marrow Transplantation registry we analyzed outcome of 1690 patients transplanted in CR1 to determine if FAB classification provides additional prognostic value. Multivariate analysis revealed that M6/M7 patients had decreased leukemia free survival (hazard ratio (HR) of 1.41, 95% confidence interval (CI), 1.01-1.99; P = .046) in addition to increased nonrelapse mortality (NRM) rates (HR, 1.79; 95% CI, 1.06-3.01; P = .028) compared with other FAB types. In the NPM1 wt AML, NOS cohort, FAB M6/M7 was also associated with increased NRM (HR, 2.17; 95% CI, 1.14-4.16; P = .019). Finally, in FLT3-ITD + patients, multivariate analyses revealed that specific FAB types were tightly associated with adverse outcome. In conclusion, FAB classification may predict outcome following transplantation in AML, NOS patients. © 2017 Wiley Periodicals, Inc.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

Science.gov (United States)

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multivariate Cholesky models of human female fertility patterns in the NLSY.

Science.gov (United States)

Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

2007-03-01

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

Simplicial band depth for multivariate functional data

KAUST Repository

López-Pintado, Sara

2014-03-05

We propose notions of simplicial band depth for multivariate functional data that extend the univariate functional band depth. The proposed simplicial band depths provide simple and natural criteria to measure the centrality of a trajectory within a sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation study shows the robustness of this new definition of depth and the advantages of using a multivariate depth versus the marginal depths for detecting outliers. Real data examples from growth curves and signature data are used to illustrate the performance and usefulness of the proposed depths. © 2014 Springer-Verlag Berlin Heidelberg.

An architecture for implementation of multivariable controllers

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Stoustrup, Jakob

1999-01-01

Browse > Conferences> American Control Conference, Prev | Back to Results | Next » An architecture for implementation of multivariable controllers 786292 searchabstract Niemann, H. ; Stoustrup, J. ; Dept. of Autom., Tech. Univ., Lyngby This paper appears in: American Control Conference, 1999....... Proceedings of the 1999 Issue Date : 1999 Volume : 6 On page(s): 4029 - 4033 vol.6 Location: San Diego, CA Meeting Date : 02 Jun 1999-04 Jun 1999 Print ISBN: 0-7803-4990-3 References Cited: 7 INSPEC Accession Number: 6403075 Digital Object Identifier : 10.1109/ACC.1999.786292 Date of Current Version : 06...... august 2002 Abstract An architecture for implementation of multivariable controllers is presented in this paper. The architecture is based on the Youla-Jabr-Bongiorno-Kucera parameterization of all stabilizing controllers. By using this architecture for implementation of multivariable controllers...

Multivariate performance reliability prediction in real-time

International Nuclear Information System (INIS)

Lu, S.; Lu, H.; Kolarik, W.J.

2001-01-01

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique

Drugs, Recipes, Babies, Bathwater, and Psychotherapy Process-Outcome Relations.

Science.gov (United States)

Stiles, William B.

1994-01-01

Responds to critiques of Stiles and Shapiro's (1994) discussion of process-outcome correlation problem by Silberschatz (1994) and Sechrest (1994). Contends that, contrary to Silberschatz's and Sechrest's multivariate suggestions, problem is not in measures or analyses but in interpretation of results, particularly in failure to incorporate fully…

Multivariate Local Polynomial Regression with Application to Shenzhen Component Index

Directory of Open Access Journals (Sweden)

Liyun Su

2011-01-01

Full Text Available This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.

Optimization of Interior Permanent Magnet Motor by Quality Engineering and Multivariate Analysis

Science.gov (United States)

Okada, Yukihiro; Kawase, Yoshihiro

This paper has described the method of optimization based on the finite element method. The quality engineering and the multivariable analysis are used as the optimization technique. This optimizing method consists of two steps. At Step.1, the influence of parameters for output is obtained quantitatively, at Step.2, the number of calculation by the FEM can be cut down. That is, the optimal combination of the design parameters, which satisfies the required characteristic, can be searched for efficiently. In addition, this method is applied to a design of IPM motor to reduce the torque ripple. The final shape can maintain average torque and cut down the torque ripple 65%. Furthermore, the amount of permanent magnets can be reduced.

An Improvement of the Hotelling T2 Statistic in Monitoring Multivariate Quality Characteristics

Directory of Open Access Journals (Sweden)

Ashkan Shabbak

2012-01-01

Full Text Available The Hotelling T2 statistic is the most popular statistic used in multivariate control charts to monitor multiple qualities. However, this statistic is easily affected by the existence of more than one outlier in the data set. To rectify this problem, robust control charts, which are based on the minimum volume ellipsoid and the minimum covariance determinant, have been proposed. Most researchers assess the performance of multivariate control charts based on the number of signals without paying much attention to whether those signals are really outliers. With due respect, we propose to evaluate control charts not only based on the number of detected outliers but also with respect to their correct positions. In this paper, an Upper Control Limit based on the median and the median absolute deviation is also proposed. The results of this study signify that the proposed Upper Control Limit improves the detection of correct outliers but that it suffers from a swamping effect when the positions of outliers are not taken into consideration. Finally, a robust control chart based on the diagnostic robust generalised potential procedure is introduced to remedy this drawback.

COMT Val158Met polymorphism is associated with post-traumatic stress disorder and functional outcome following mild traumatic brain injury.

Science.gov (United States)

Winkler, Ethan A; Yue, John K; Ferguson, Adam R; Temkin, Nancy R; Stein, Murray B; Barber, Jason; Yuh, Esther L; Sharma, Sourabh; Satris, Gabriela G; McAllister, Thomas W; Rosand, Jonathan; Sorani, Marco D; Lingsma, Hester F; Tarapore, Phiroz E; Burchard, Esteban G; Hu, Donglei; Eng, Celeste; Wang, Kevin K W; Mukherjee, Pratik; Okonkwo, David O; Diaz-Arrastia, Ramon; Manley, Geoffrey T

2017-01-01

Mild traumatic brain injury (mTBI) results in variable clinical trajectories and outcomes. The source of variability remains unclear, but may involve genetic variations, such as single nucleotide polymorphisms (SNPs). A SNP in catechol-o-methyltransferase (COMT) is suggested to influence development of post-traumatic stress disorder (PTSD), but its role in TBI remains unclear. Here, we utilize the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study to investigate whether the COMT Val 158 Met polymorphism is associated with PTSD and global functional outcome as measured by the PTSD Checklist - Civilian Version and Glasgow Outcome Scale Extended (GOSE), respectively. Results in 93 predominately Caucasian subjects with mTBI show that the COMT Met 158 allele is associated with lower incidence of PTSD (univariate odds ratio (OR) of 0.25, 95% CI [0.09-0.69]) and higher GOSE scores (univariate OR 2.87, 95% CI [1.20-6.86]) 6-months following injury. The COMT Val 158 Met genotype and PTSD association persists after controlling for race (multivariable OR of 0.29, 95% CI [0.10-0.83]) and pre-existing psychiatric disorders/substance abuse (multivariable OR of 0.32, 95% CI [0.11-0.97]). PTSD emerged as a strong predictor of poorer outcome on GOSE (multivariable OR 0.09, 95% CI [0.03-0.26]), which persists after controlling for age, GCS, and race. When accounting for PTSD in multivariable analysis, the association of COMT genotype and GOSE did not remain significant (multivariable OR 1.73, 95% CI [0.69-4.35]). Whether COMT genotype indirectly influences global functional outcome through PTSD remains to be determined and larger studies in more diverse populations are needed to confirm these findings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

International Nuclear Information System (INIS)

Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.

2008-01-01

Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method

A longitudinal analysis of nursing home outcomes.

Science.gov (United States)

Porell, F; Caro, F G; Silva, A; Monane, M

1998-10-01

To investigate resident and facility attributes associated with long-term care health outcomes in nursing homes. Quarterly Management Minutes Questionnaire (MMQ) survey data for Medicaid case-mix reimbursement of nursing homes in Massachusetts from 1991 to 1994, for specification of outcomes and resident attributes. Facility attributes are specified from cost report data. Multivariate logistic and "state-dependence" regression models are estimated for survival, ADL functional status, incontinence status, and mental status outcomes from longitudinal residence histories of Medicaid residents spanning 3 to 36 months in length. Outcomes are specified to be a function of resident demographic and diagnostic attributes and facility-level operating and nurse staffing attributes. The estimated parameters for resident demographic and diagnostic attributes showed a great deal of construct validity with respect to clinical expectations regarding risk factors for adverse outcomes. Few facility attributes were associated with outcomes generally, and none was significantly associated with all four outcomes. The absence of uniform associations between facility attributes and the various long-term care health outcomes studied suggests that strong facility performance on one health outcome may coexist with much weaker performance on other outcomes. This has implications for the aggregation of individual facility performance measures on multiple outcomes and the development of overall outcome performance measures.

I - Multivariate Classification and Machine Learning in HEP

CERN Multimedia

CERN. Geneva

2016-01-01

Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation.

Science.gov (United States)

Lourenço, Vera; Herdling, Thorsten; Reich, Gabriele; Menezes, José C; Lochmann, Dirk

2011-08-01

A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles. Copyright © 2011. Published by Elsevier B.V.

Collateral flow as causative of good outcomes in endovascular stroke therapy.

Science.gov (United States)

Sheth, Sunil A; Sanossian, Nerses; Hao, Qing; Starkman, Sidney; Ali, Latisha K; Kim, Doojin; Gonzalez, Nestor R; Tateshima, Satoshi; Jahan, Reza; Duckwiler, Gary R; Saver, Jeffrey L; Vinuela, Fernando; Liebeskind, David S

2016-01-01

Endovascular reperfusion techniques are a promising intervention for acute ischemic stroke (AIS). Prior studies have identified markers of initial injury (arrival NIH stroke scale (NIHSS) or infarct volume) as predictive of outcome after these procedures. We sought to define the role of collateral flow at the time of presentation in determining the extent of initial ischemic injury and its influence on final outcome. Demographic, clinical, laboratory, and radiographic data were prospectively collected on a consecutive cohort of patients who received endovascular therapy for acute cerebral ischemia at a single tertiary referral center from September 2004 to August 2010. Higher collateral grade as assessed by the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) grading scheme on angiography at the time of presentation was associated with improved reperfusion rates after endovascular intervention, decreased post-procedural hemorrhage, smaller infarcts on presentation and discharge, as well as improved neurological function on arrival to the hospital, discharge, and 90 days later. Patients matched by vessel occlusion, age, and time of onset demonstrated smaller strokes on presentation and better functional and radiographic outcome if found to have superior collateral flow. In multivariate analysis, lower collateral grade independently predicted higher NIHSS on arrival. Improved collateral flow in patients with AIS undergoing endovascular therapy was associated with improved radiographic and clinical outcomes. Independent of age, vessel occlusion and time, in patients with comparable ischemic burdens, changes in collateral grade alone led to significant differences in initial stroke severity as well as ultimate clinical outcome. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A Newton Algorithm for Multivariate Total Least Squares Problems

Directory of Open Access Journals (Sweden)

WANG Leyang

2016-04-01

Full Text Available In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate that the Newton algorithm for multivariate total least squares problems could be practiced and have higher convergence rate.

Factors affecting outcome in myasthenia gravis.

Science.gov (United States)

Andersen, Jintana B; Gilhus, Nils Erik; Sanders, Donald B

2016-12-01

Information from myasthenia gravis (MG) patients treated and evaluated for at least 2 years between 1980 and 2014 was reviewed to assess the effect of demographics, antibody status and titer, thymus histology, and clinical severity on outcome after 2, 5, and 10 years of treatment. Among 268 patients, 74% had acetylcholine receptor antibodies, 5% had muscle specific tyrosine kinase-antibodies, and 22% had neither. Optimal outcome was achieved by 64% of patients at 2 years of follow-up, 73% at 5 years, and 75% after 10 years. Optimal outcome was achieved more often in patients with late onset, in those who had thymectomy, and in those with ocular-only disease at maximum severity. The only consistent independent predictor of optimal outcome was onset after age 50 years on multivariate analysis. Prognosis is favorable for the majority of MG patients, regardless of age, maximum disease severity, or antibody status. Muscle Nerve, 2016 Muscle Nerve 54: 1041-1049, 2016. © 2016 Wiley Periodicals, Inc.

Application of multivariate splines to discrete mathematics

OpenAIRE

Xu, Zhiqiang

2005-01-01

Using methods developed in multivariate splines, we present an explicit formula for discrete truncated powers, which are defined as the number of non-negative integer solutions of linear Diophantine equations. We further use the formula to study some classical problems in discrete mathematics as follows. First, we extend the partition function of integers in number theory. Second, we exploit the relation between the relative volume of convex polytopes and multivariate truncated powers and giv...

The q-Onsager algebra and multivariable q-special functions

Science.gov (United States)

Baseilhac, Pascal; Vinet, Luc; Zhedanov, Alexei

2017-09-01

Two sets of mutually commuting q-difference operators x i and y j , i,j=1,...,N such that x i and y i generate a homomorphic image of the q-Onsager algebra for each i are introduced. The common polynomial eigenfunctions of each set are found to be entangled product of elementary Pochhammer functions in N variables and N+3 parameters. Under certain conditions on the parameters, they form two ‘dual’ bases of polynomials in N variables. The action of each operator with respect to its dual basis is block tridiagonal. The overlap coefficients between the two dual bases are expressed as entangled products of q-Racah polynomials and satisfy an orthogonality relation. The overlap coefficients between either one of these bases and the multivariable monomial basis are also considered. One obtains in this case entangled products of dual q-Krawtchouk polynomials. Finally, the ‘split’ basis in which the two families of operators act as block bidiagonal matrices is also provided.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

Science.gov (United States)

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

Optimism Moderates the Influence of Pain Catastrophizing on Shoulder Pain Outcome: A Longitudinal Analysis.

Science.gov (United States)

Coronado, Rogelio A; Simon, Corey B; Lentz, Trevor A; Gay, Charles W; Mackie, Lauren N; George, Steven Z

2017-01-01

Study Design Secondary analysis of prospectively collected data. Background An abundance of evidence has highlighted the influence of pain catastrophizing and fear avoidance on clinical outcomes. Less is known about the interaction of positive psychological resources with these pain-associated distress factors. Objective To assess whether optimism moderates the influence of pain catastrophizing and fear avoidance on 3-month clinical outcomes in patients with shoulder pain. Methods Data from 63 individuals with shoulder pain (mean ± SD age, 38.8 ± 14.9 years; 30 female) were examined. Demographic, psychological, and clinical characteristics were obtained at baseline. Validated measures were used to assess optimism (Life Orientation Test-Revised), pain catastrophizing (Pain Catastrophizing Scale), fear avoidance (Fear-Avoidance Beliefs Questionnaire physical activity subscale), shoulder pain intensity (Brief Pain Inventory), and shoulder function (Pennsylvania Shoulder Score function subscale). Shoulder pain and function were reassessed at 3 months. Regression models assessed the influence of (1) pain catastrophizing and optimism and (2) fear avoidance and optimism. The final multivariable models controlled for factors of age, sex, education, and baseline scores, and included 3-month pain intensity and function as separate dependent variables. Results Shoulder pain (mean difference, -1.6; 95% confidence interval [CI]: -2.1, -1.2) and function (mean difference, 2.4; 95% CI: 0.3, 4.4) improved over 3 months. In multivariable analyses, there was an interaction between pain catastrophizing and optimism (β = 0.19; 95% CI: 0.02, 0.35) for predicting 3-month shoulder function (F = 16.8, R 2 = 0.69, Poptimism lessened the influence of pain catastrophizing on function. There was no evidence of significant moderation of fear-avoidance beliefs for 3-month shoulder pain (P = .090) or function (P = .092). Conclusion Optimism decreased the negative influence of pain

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi

2014-01-01

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both

Specific Infectious Organisms Associated With Poor Outcomes in Treatment for Hip Periprosthetic Infection.

Science.gov (United States)

Cunningham, Daniel J; Kavolus, Joseph J; Bolognesi, Michael P; Wellman, Samuel S; Seyler, Thorsten M

2017-06-01

Periprosthetic hip infection treatment remains a significant challenge for orthopedics. Some studies have suggested that methicillin resistance and gram-negative organism type are associated with increased treatment failure. The aim of this research is to determine if specific organisms were associated with poor outcomes in treatment for hip periprosthetic infection. Records were reviewed of all patients between 2005 and 2015 who underwent treatment for infected partial or total hip arthroplasty. Characteristics of each patient's treatment course were determined including baseline characteristics, infecting organism(s), infection status at final follow-up, surgeries for infection, and time in hospital. Baseline characteristics and organisms that were associated with clinical outcomes in univariate analysis were incorporated into multivariable outcomes models. When compared with patients infected with other organism(s), patients infected with the following organisms had significantly decreased infection-free rates: Pseudomonas, methicillin-resistant Staphylococcus aureus (MRSA), and Proteus. Infection with certain organisms was associated with 1.13-2.58 additional surgeries: methicillin-sensitive S aureus, coagulase-negative Staphylococcus, MRSA, Pseudomonas, Peptostreptococcus, Klebsiella, Candida, diphtheroids, Propionibacterium acnes, and Proteus species. Specific organisms were associated with 8.56-24.54 additional days in hospital for infection: methicillin-sensitive S aureus, coagulase-negative Staphylococcus, Proteus, MRSA, Enterococcus, Pseudomonas, Klebsiella, beta-hemolytic Streptococcus, and diphtheroids. Higher comorbidity score was also associated with greater length of hospitalization. MRSA, Pseudomonas, and Proteus were associated with all 3 outcomes of lower infection-free rate, more surgery, and more time in hospital in treatment for hip periprosthetic infection. Organism-specific outcome information may help individualize patient

Outcome of temporal lobe epilepsy surgery evaluated with bitemporal intracranial electrode recordings.

Science.gov (United States)

Massot-Tarrús, Andreu; Steven, David A; McLachlan, Richard S; Mirsattari, Seyed M; Diosy, David; Parrent, Andrew G; Blume, Warren T; Girvin, John P; Burneo, Jorge G

2016-11-01

Temporal lobe epilepsy (TLE) with unclear lateralization may require intracranial implantation of electrodes (IIE). We retrospectively assessed the association between the use of IIE and long-term outcomes in patients undergoing anterior temporal lobectomy (ATL). We retrospectively reviewed the records of 1,032 patients undergoing epilepsy surgery at our center from 1977 to 2006. Patients who underwent ATL were included. Seizure outcome was assessed through final follow-up. Those who underwent scalp and IIE (mostly evaluated with temporal subdural strip electrodes) were compared. From 497 patients who underwent ATL, 139 did so after IIE placement in the temporal lobes. Mean age at surgery was 32.3±12.3years and median duration of follow-up 24 months (range: 6-36). Fifty-three percent of those evaluated with IIE were seizure-free at their last available visit (vs. 68% evaluated with only scalp EEG, p=0.002). Patients with lesional TLE generally had a better outcome (65.5% seizure free) than those without lesions (56.3%, p=0.093), especially for unilateral TLE diagnosed with IIE. In a multivariate Cox regression analyses adjusted for gender, neuropsychological concordance, pathological findings, and post-operative seizures, bilateral TLE predicted seizure recurrence in IIE patients (HR=2.08, 95% CI: 1.08-4.0, p=0.029). More than a half of those who undergo IIE in suspected TLE are seizure free after ATL. IIE allows for the identification of surgical candidates. Copyright © 2016 Elsevier B.V. All rights reserved.

The evolution of multivariate maternal effects.

Directory of Open Access Journals (Sweden)

Bram Kuijper

2014-04-01

Full Text Available There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

The evolution of multivariate maternal effects.

Science.gov (United States)

Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

2014-04-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

Multivariate generalized linear mixed models using R

CERN Document Server

Berridge, Damon Mark

2011-01-01

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

Science.gov (United States)

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

Multivariate Regression Analysis and Slaughter Livestock,

Science.gov (United States)

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

(Anti)symmetric multivariate exponential functions and corresponding Fourier transforms

International Nuclear Information System (INIS)

Klimyk, A U; Patera, J

2007-01-01

We define and study symmetrized and antisymmetrized multivariate exponential functions. They are defined as determinants and antideterminants of matrices whose entries are exponential functions of one variable. These functions are eigenfunctions of the Laplace operator on the corresponding fundamental domains satisfying certain boundary conditions. To symmetric and antisymmetric multivariate exponential functions there correspond Fourier transforms. There are three types of such Fourier transforms: expansions into the corresponding Fourier series, integral Fourier transforms and multivariate finite Fourier transforms. Eigenfunctions of the integral Fourier transforms are found

Long-term outcome of pronation-external rotation ankle fractures treated with syndesmotic screws only.

Science.gov (United States)

Lambers, Kaj T A; van den Bekerom, Michel P J; Doornberg, Job N; Stufkens, Sjoerd A S; van Dijk, C Niek; Kloen, Peter

2013-09-04

There is sparse information in the literature on the outcome of Maisonneuve-type pronation-external rotation ankle fractures treated with syndesmotic screws. The primary aim of this study was to determine the long-term results of such treatment of these fractures as indicated by standardized patient-based and physician-based outcome measures. The secondary aim was to identify predictors of the outcome with use of bivariate and multivariate statistical analysis. Fifty patients with pronation-external rotation (predominantly Maisonneuve) fractures were treated with open reduction and internal fixation of the syndesmosis utilizing only one or two screws. The results were evaluated at a mean of twenty-one years after the fracture utilizing three standardized outcomes instruments: (1) the Foot and Ankle Ability Measure (FAAM), (2) the American Orthopaedic Foot & Ankle Society (AOFAS) ankle-hindfoot scale, and (3) the Center for Epidemiologic Studies-Depression (CES-D) Scale. Osteoarthritis was graded according to the van Dijk and revised Takakura radiographic scoring systems. Bivariate and multivariate analyses were performed to identify predictors of long-term outcome. Forty-four (92%) of forty-eighty patients had good or excellent AOFAS scores, and forty-four (90%) of forty-nine had good or excellent FAAM scores. Arthrodesis for severe osteoarthritis was performed in two patients. Radiographic evidence of osteoarthritis was observed in twenty-four (49%) of forty-nine patients. Multivariate analysis identified pain as the most important independent predictor of long-term ankle function as indicated by the AOFAS and FAAM scores, explaining 91% and 53% of the variation in scores, respectively. Analysis of pain as the dependent variable in bivariate analyses revealed that depression, ankle range of motion, and a subsequent surgery were significantly correlated with higher pain scores. No firm conclusions could be drawn after multivariate analysis of predictors of pain

Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose-response

International Nuclear Information System (INIS)

Skwarchuk, Mark W.; Jackson, Andrew; Zelefsky, Michael J.; Venkatraman, Ennapadam S.; Cowen, Didier M.; Levegruen, Sabine; Burman, Chandra M.; Fuks, Zvi; Leibel, Steven A.; Ling, C. Clifton

2000-01-01

Purpose: The purpose of this paper is to use the outcome of a dose escalation protocol for three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer to study the dose-response for late rectal toxicity and to identify anatomic, dosimetric, and clinical factors that correlate with late rectal bleeding in multivariate analysis. Methods and Materials: Seven hundred forty-three patients with T1c-T3 prostate cancer were treated with 3D-CRT with prescribed doses of 64.8 to 81.0 Gy. The 5-year actuarial rate of late rectal toxicity was assessed using Kaplan-Meier statistics. A retrospective dosimetric analysis was performed for patients treated to 70.2 Gy (52 patients) or 75.6 Gy (119 patients) who either exhibited late rectal bleeding (RTOG Grade 2/3) within 30 months after treatment (i.e., 70.2 Gy--13 patients, 75.6 Gy--36 patients) or were nonbleeding for at least 30 months (i.e., 70.2 Gy--39 patients, 75.6 Gy--83 patients). Univariate and multivariate logistic regression was performed to correlate late rectal bleeding with several anatomic, dosimetric, and clinical variables. Results: A dose response for ≥ Grade 2 late rectal toxicity was observed. By multivariate analysis, the following factors were significantly correlated with ≥ Grade 2 late rectal bleeding for patients prescribed 70.2 Gy: 1) enclosure of the outer rectal contour by the 50% isodose on the isocenter slice (i.e., Iso50) (p max (p max

Scale and shape mixtures of multivariate skew-normal distributions

KAUST Repository

Arellano-Valle, Reinaldo B.; Ferreira, Clé cio S.; Genton, Marc G.

2018-01-01

We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down

Banach frames for multivariate alpha-modulation spaces

DEFF Research Database (Denmark)

Borup, Lasse; Nielsen, Morten

2006-01-01

The α-modulation spaces [$Mathematical Term$], form a family of spaces that include the Besov and modulation spaces as special cases. This paper is concerned with construction of Banach frames for α-modulation spaces in the multivariate setting. The frames constructed are unions of independent Ri...... Riesz sequences based on tensor products of univariate brushlet functions, which simplifies the analysis of the full frame. We show that the multivariate α-modulation spaces can be completely characterized by the Banach frames constructed....

An uncertain journey around the tails of multivariate hydrological distributions

Science.gov (United States)

Serinaldi, Francesco

2013-10-01

Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.

Men and women show similar survival outcome in stage IV breast cancer.

Science.gov (United States)

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Status self-validation of a multifunctional sensor using a multivariate relevance vector machine and predictive filters

International Nuclear Information System (INIS)

Shen, Zhengguang; Wang, Qi

2013-01-01

A novel strategy by using a multivariable relevance vector machine coupled with predictive filters for status self-validation of a multifunctional sensor is proposed. The working principle and online updating algorithm of predictive filters are emphasized for multiple fault detection, isolation and recovery (FDIR), and the incorrect sensor measurements are validated online. The multivariable relevance vector machine is then employed for the signal reconstruction of the multifunctional sensor to generate the final validated measurement values (VMV) of multiple measured components, in which its advantages of sparse models and multivariable simultaneous outputs are fully used. With all likely uncertainty sources of the multifunctional self-validating sensor taken into account, the uncertainty propagation model is deduced in detail to evaluate the online validated uncertainty (VU) under a fault-free situation while a qualitative uncertainty component is appended to indicate the accuracy changes of VMV under different types of fault. A real experimental system of a multifunctional self-validating sensor is designed to verify the performance of the proposed strategy. From the real-time capacity and fault recovery accuracy of FDIR, and runtime of signal reconstruction under small samples, a performance comparison among different methods is made. Results demonstrate that the proposed scheme provides a better solution to the status self-validation of a multifunctional self-validating sensor under both normal and abnormal situations. (paper)

A multivariate extension of mutual information for growing neural networks.

Science.gov (United States)

Ball, Kenneth R; Grant, Christopher; Mundy, William R; Shafer, Timothy J

2017-11-01

Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearson's correlation), by statistics estimated from cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are designed to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. The NMI approach is applied to these simulations and also to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Finally, NMI is a scalar measure of global (rather than pairwise) mutual information in a multivariate network, and hence relies on less assumptions for cross-network comparisons than historical approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Oil and stock market volatility: A multivariate stochastic volatility perspective

International Nuclear Information System (INIS)

Vo, Minh

2011-01-01

This paper models the volatility of stock and oil futures markets using the multivariate stochastic volatility structure in an attempt to extract information intertwined in both markets for risk prediction. It offers four major findings. First, the stock and oil futures prices are inter-related. Their correlation follows a time-varying dynamic process and tends to increase when the markets are more volatile. Second, conditioned on the past information, the volatility in each market is very persistent, i.e., it varies in a predictable manner. Third, there is inter-market dependence in volatility. Innovations that hit either market can affect the volatility in the other market. In other words, conditioned on the persistence and the past volatility in their respective markets, the past volatility of the stock (oil futures) market also has predictive power over the future volatility of the oil futures (stock) market. Finally, the model produces more accurate Value-at-Risk estimates than other benchmarks commonly used in the financial industry. - Research Highlights: → This paper models the volatility of stock and oil futures markets using the multivariate stochastic volatility model. → The correlation between the two markets follows a time-varying dynamic process which tends to increase when the markets are more volatile. → The volatility in each market is very persistent. → Innovations that hit either market can affect the volatility in the other market. → The model produces more accurate Value-at-Risk estimates than other benchmarks commonly used in the financial industry.

Energy and economic growth in the USA: a multivariate approach

International Nuclear Information System (INIS)

Stern, D.I.

1993-01-01

This paper examines the casual relationship between Gross Domestic Product and energy use for the period 1947-90 in the United States of America. The relationship between energy use and economic growth has been examined by both biophysical and neoclassical economists. In particular, several studies have tested for the presence of a causal relationships (in the Granger sense) between energy use and economic growth. However, these tests do not allow a direct test of the relative explanatory powers of the neoclassical and biophysical models. A multivariate adaptation of the test-vector autoregression (VAR) does allow such a test. A VAR of GDP, energy use, capital stock and employment is estimated and Granger tests for causal relationships between the variables are carried out. Although there is no evidence that gross energy use Granger causes GDP, a measure of final energy use adjusted for changing fuel composition does Granger cause GDP. (author)

Multivariate statistical analysis a high-dimensional approach

CERN Document Server

Serdobolskii, V

2000-01-01

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

Directory of Open Access Journals (Sweden)

Elza Jurun

2012-12-01

Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions

KAUST Repository

Arellano-Valle, Reinaldo B.

2012-02-27

The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.

Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions

KAUST Repository

Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Genton, Marc G.

2012-01-01

The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.

Fetal sex modifies effects of prenatal stress exposure and adverse birth outcomes.

Science.gov (United States)

Wainstock, Tamar; Shoham-Vardi, Ilana; Glasser, Saralee; Anteby, Eyal; Lerner-Geva, Liat

2015-01-01

Prenatal maternal stress is associated with pregnancy complications, poor fetal development and poor birth outcomes. Fetal sex has also been shown to affect the course of pregnancy and its outcomes. The aim of this study was to evaluate whether fetal sex modifies the association between continuous exposure to life-threatening rocket attack alarms and adverse pregnancy outcomes. A retrospective cohort study was conducted in which the exposed group was comprised of 1846 women exposed to rocket-attack alarms before and during pregnancy. The unexposed group, with similar sociodemographic characteristics, delivered during the same period of time at the same medical center, but resided out of rocket-attack range. Multivariable models for each gender separately, controlling for possible confounders, evaluated the risk associated with exposure for preterm births (PTB), low birthweight (LBW), small for gestational age and small head circumference (HC). In both univariable and multivariable analyses exposure status was a significant risk factor in female fetuses only: PTB (adj. OR = 1.43; 1.04-1.96), LBW (adj. OR = 1.41; 1.02-1.95) and HC stress.

A baseline for the multivariate comparison of resting state networks

Directory of Open Access Journals (Sweden)

Elena A Allen

2011-02-01

Full Text Available As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting state networks of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12 to 71 years. Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. Resting state networks were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

A Baseline for the Multivariate Comparison of Resting-State Networks

Science.gov (United States)

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

On The Structure of The Inverse of a Linear Constant Multivariable ...

African Journals Online (AJOL)

On The Structure of The Inverse of a Linear Constant Multivariable System. ... It is shown that the use of this representation has certain advantages in the design of multivariable feedback systems. typical examples were considered to indicate the corresponding application. Keywords: Stability Functions, multivariable ...

Directional outlyingness for multivariate functional data

KAUST Repository

Dai, Wenlin

2018-04-07

The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

Science.gov (United States)

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

Directory of Open Access Journals (Sweden)

Qiong Yang

2012-01-01

Full Text Available Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical or different types of components (e.g., some are continuous and others are categorical. We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Stroke Location Is an Independent Predictor of Cognitive Outcome.

Science.gov (United States)

Munsch, Fanny; Sagnier, Sharmila; Asselineau, Julien; Bigourdan, Antoine; Guttmann, Charles R; Debruxelles, Sabrina; Poli, Mathilde; Renou, Pauline; Perez, Paul; Dousset, Vincent; Sibon, Igor; Tourdias, Thomas

2016-01-01

On top of functional outcome, accurate prediction of cognitive outcome for stroke patients is an unmet need with major implications for clinical management. We investigated whether stroke location may contribute independent prognostic value to multifactorial predictive models of functional and cognitive outcomes. Four hundred twenty-eight consecutive patients with ischemic stroke were prospectively assessed with magnetic resonance imaging at 24 to 72 hours and at 3 months for functional outcome using the modified Rankin Scale and cognitive outcome using the Montreal Cognitive Assessment (MoCA). Statistical maps of functional and cognitive eloquent regions were derived from the first 215 patients (development sample) using voxel-based lesion-symptom mapping. We used multivariate logistic regression models to study the influence of stroke location (number of eloquent voxels from voxel-based lesion-symptom mapping maps), age, initial National Institutes of Health Stroke Scale and stroke volume on modified Rankin Scale and MoCA. The second part of our cohort was used as an independent replication sample. In univariate analyses, stroke location, age, initial National Institutes of Health Stroke Scale, and stroke volume were all predictive of poor modified Rankin Scale and MoCA. In multivariable analyses, stroke location remained the strongest independent predictor of MoCA and significantly improved the prediction compared with using only age, initial National Institutes of Health Stroke Scale, and stroke volume (area under the curve increased from 0.697-0.771; difference=0.073; 95% confidence interval, 0.008-0.155). In contrast, stroke location did not persist as independent predictor of modified Rankin Scale that was mainly driven by initial National Institutes of Health Stroke Scale (area under the curve going from 0.840 to 0.835). Similar results were obtained in the replication sample. Stroke location is an independent predictor of cognitive outcome (MoCA) at 3

Determination of sulfamethoxazole and trimethoprim mixtures by multivariate electronic spectroscopy

OpenAIRE

Cordeiro, Gilcélia A.; Peralta-Zamora, Patricio; Nagata, Noemi; Pontarollo, Roberto

2008-01-01

In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate model...

Blood glucose level and outcome after cardiac arrest: insights from a large registry in the hypothermia era.

Science.gov (United States)

Daviaud, Fabrice; Dumas, Florence; Demars, Nadège; Geri, Guillaume; Bouglé, Adrien; Morichau-Beauchant, Tristan; Nguyen, Yên-Lan; Bougouin, Wulfran; Pène, Frédéric; Charpentier, Julien; Cariou, Alain

2014-06-01

The influence of blood glucose (BG) level during the post-resuscitation period after out-of-hospital cardiac arrest (OHCA) is still debated. To evaluate the relationship between blood glucose level and outcome, we included the median glycemia and its maximal amplitude over the first 48 h following ICU admission in an analysis of outcome predictors. We conducted a database study in a cardiac arrest center in Paris, France. Between 2006 and 2010, we included 381 patients who were all resuscitated from an OHCA. A moderate glycemic control was applied in all patients. The median glycemia and the largest change over the first 48 h were included in a multivariate analysis that was performed to determine parameters associated with a favorable outcome. Of the 381 patients, 136 (36 %) had a favorable outcome (CPC 1-2). Median BG level was 7.6 mmol/L (6.3-9.8) in patients with a favorable outcome compared to 9.0 mmol/L (IQR 7.1-10.6) for patients with an unfavorable outcome (p level variation was 7.1 (4.2-11) and 9.6 (5.9-13.6) mmol/L in patients with and without a favorable outcome, respectively (p level over the first 48 h was found to be an independent predictor of poor issue [OR = 0.43; 95 % CI (0.24-0.78), p = 0.006]. Finally a progressive increase in median BG level was associated with a progressive increase in the proportion of patients with a poor outcome. We observed a relationship between high blood glucose level and outcome after cardiac arrest. These results suggest the need to test a strategy combining both control of glycemia and minimization of glycemic variations for its ability to improve post-resuscitation care.

Optimal non-periodic inspection for a multivariate degradation model

NARCIS (Netherlands)

Barker, C.T.; Newby, M.J.

2009-01-01

We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined

A Range-Based Multivariate Model for Exchange Rate Volatility

NARCIS (Netherlands)

B. Tims (Ben); R.J. Mahieu (Ronald)

2003-01-01

textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are

Multivariate analysis of 2-DE protein patterns - Practical approaches

DEFF Research Database (Denmark)

Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

2007-01-01

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

Selenium deficiency and pregnancy outcome in pregnant women with HIV in Lagos, Nigeria.

Science.gov (United States)

Okunade, Kehinde S; Olowoselu, Olusola F; Osanyin, Gbemisola E; John-Olabode, Sarah; Akanmu, Sulaimon A; Anorlu, Rose I

2018-04-16

To investigate the prevalence of maternal selenium deficiency and its effects on pregnancy outcomes in pregnant women with HIV in Lagos, Nigeria. The present descriptive cross-sectional study enrolled women aged 15-49 years with HIV who were at 14-26 weeks of a singleton pregnancy and were attending Lagos University Teaching Hospital, Lagos, Nigeria, between August 1, 2016, and April 30, 2017. Participants were selected by consecutive sampling and baseline data were collected through interviews. Venous blood samples were obtained to measure selenium concentrations, and associations between low maternal selenium concentrations (defined as <0.89 μmol/L) and pregnancy outcomes were examined using bivariate and multivariate analysis. The final analysis included 113 patients; selenium deficiency was recorded in 23 (20.4%) patients. Women with selenium deficiency had an approximately eight-fold higher risk of preterm delivery (adjusted odds ratio 7.61, 95% confidence interval 4.37-18.89; P=0.031) and of delivering a term neonate with a low delivery weight (adjusted odds ratio 8.11, 95% confidence interval 3.27-17.22; P=0.012), compared with women with a normal selenium concentration. The prevalence of selenium deficiency among pregnant women with HIV in Lagos was relatively high. The significant associations observed between maternal selenium deficiency and adverse pregnancy outcomes could have implications for the future management of HIV in pregnancy. © 2018 International Federation of Gynecology and Obstetrics.

Introduction to multivariate discrimination

Science.gov (United States)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

Introduction to multivariate discrimination

International Nuclear Information System (INIS)

Kegl, B.

2013-01-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyper-parameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

International Nuclear Information System (INIS)

Garcia-Cabrejo, Oscar; Valocchi, Albert

2014-01-01

Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE

Correlation between the histological features of corneal surface pannus following ocular surface burns and the final outcome of cultivated limbal epithelial transplantation.

Science.gov (United States)

Sati, Alok; Basu, Sayan; Sangwan, Virender S; Vemuganti, Geeta K

2015-04-01

To report the influence of histological features of corneal surface pannus following ocular surface burn on the outcome of cultivated limbal epithelial transplantation (CLET). On retrospectively reviewing the medical records of the patients who underwent autologous CLET from April 2002 to June 2012 at L V Prasad Eye Institute, Hyderabad, India, we could trace the histological reports in only 90 records. These 90 records, besides clinical parameters, were reviewed for the influence of various histological features on the final outcome of CLET. The histological features include epithelial hyperplasia (21.1%), surface ulceration (2.2%), goblet cells (62.2%), squamous metaplasia (11.1%), active fibrosis (31.1%), severe inflammation (8.9%), multinucleated giant cells (3.3%), stromal calcification (8.9%) and active proliferating vessels (5.6%). Among these histological features, patients with either hyperplasia or calcification in their excised corneal pannus show an unfavourable outcome compared with patients without hyperplasia (p=0.003) or calcification (p=0.018). A similar unfavourable outcome was not seen with other histological features and various clinical parameters. Presence of either calcific deposits or hyperplasia in the excised corneal pannus provides poor prognostication; hence, a proper counselling of such patients is mandatory along with a close follow-up. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Acanthamoeba keratitis in 194 patients: risk factors for bad outcomes and severe inflammatory complications.

Science.gov (United States)

Carnt, Nicole; Robaei, Dana; Minassian, Darwin C; Dart, John K G

2018-01-03

To determine demographic and clinical features of patients with Acanthamoeba keratitis (AK) that are independent risk factors both for bad outcomes and for severe inflammatory complications (SIC). A retrospective audit of medical records of AK cases at Moorfields Eye Hospital from July 2000 to April 2012, including 12 earlier surgical cases. Cases with a bad outcome were defined as those having one or more of the following: corneal perforation, keratoplasty, other surgery (except biopsy), duration of antiamoebic therapy (AAT) ≥10.5 months (the 75th percentile of the whole cohort) and final visual acuity ≤20/80. SICs were defined as having scleritis and/or a stromal ring infiltrate. Multivariable analysis was used to identify independent risk factors for both bad outcomes and SICs. Records of 194 eyes (194 patients) were included, having bad outcomes in 93 (48%). Bad outcomes were associated with the presence of SIC, aged >34 years, corticosteroids used before giving AAT and symptom duration >37 days before AAT. The development of SIC was independently associated with aged >34 years, corticosteroids used before giving AAT and herpes simplex virus (HSV) keratitis treatment before AAT. The prompt diagnosis of AK, avoidance of a misdiagnosis of HSV keratitis and corticosteroid use before the exclusion of AK as a potential cause of keratitis are essential to the provision of a good outcome for patients and for the avoidance of SIC. Older age is an unmodifiable risk factor that may reflect differences in the immune response to AK in this patient subset. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Multivariate phase type distributions - Applications and parameter estimation

DEFF Research Database (Denmark)

Meisch, David

The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... projects and depend on reliable cost estimates. The Successive Principle is a group analysis method primarily used for analyzing medium to large projects in relation to cost or duration. We believe that the mathematical modeling used in the Successive Principle can be improved. We suggested a novel...

Combining p-values in replicated single-case experiments with multivariate outcome.

Science.gov (United States)

Solmi, Francesca; Onghena, Patrick

2014-01-01

Interest in combining probabilities has a long history in the global statistical community. The first steps in this direction were taken by Ronald Fisher, who introduced the idea of combining p-values of independent tests to provide a global decision rule when multiple aspects of a given problem were of interest. An interesting approach to this idea of combining p-values is the one based on permutation theory. The methods belonging to this particular approach exploit the permutation distributions of the tests to be combined, and use a simple function to combine probabilities. Combining p-values finds a very interesting application in the analysis of replicated single-case experiments. In this field the focus, while comparing different treatments effects, is more articulated than when just looking at the means of the different populations. Moreover, it is often of interest to combine the results obtained on the single patients in order to get more global information about the phenomenon under study. This paper gives an overview of how the concept of combining p-values was conceived, and how it can be easily handled via permutation techniques. Finally, the method of combining p-values is applied to a simulated replicated single-case experiment, and a numerical illustration is presented.

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

Science.gov (United States)

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All

The Multivariate Gaussian Probability Distribution

DEFF Research Database (Denmark)

Ahrendt, Peter

2005-01-01

This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical ...

A Multivariate Analysis of Adverse Childhood Experiences and Health Behaviors and Outcomes among College Students

Science.gov (United States)

Windle, Michael; Haardörfer, Regine; Getachew, Beth; Shah, Jean; Payne, Jackie; Pillai, Dina; Berg, Carla J.

2018-01-01

Objective: This study investigated associations between adverse childhood experiences (ACE) prior to age 18 years and multiple health behaviors (eg, cigarette and other substance use) and outcomes (eg, obesity, depression) for a large college sample. Participants: 2,969 college students from seven universities in the state of Georgia were included…

Multivariate Process Control with Autocorrelated Data

DEFF Research Database (Denmark)

Kulahci, Murat

2011-01-01

As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control and monitoring. This new high dimensional data...... often exhibit not only cross-­‐correlation among the quality characteristics of interest but also serial dependence as a consequence of high sampling frequency and system dynamics. In practice, the most common method of monitoring multivariate data is through what is called the Hotelling’s T2 statistic....... In this paper, we discuss the effect of autocorrelation (when it is ignored) on multivariate control charts based on these methods and provide some practical suggestions and remedies to overcome this problem....

Transient multivariable sensor evaluation

Energy Technology Data Exchange (ETDEWEB)

Vilim, Richard B.; Heifetz, Alexander

2017-02-21

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

Science.gov (United States)

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

Periodontal disease and bacterial vaginosis increase the risk for adverse pregnancy outcome.

OpenAIRE

Oittinen, Juha; Kurki, Tapio; Kekki, Minnamaija; Kuusisto, Minna; Pussinen, Pirkko; Vilkuna-Rautiainen, Tiina; Nieminen, Anja; Asikainen, Sirkka; Paavonen, Jorma

2005-01-01

OBJECTIVES: To determine whether periodontal disease or bacterial vaginosis (BV) diagnosed before pregnancy increase the risk for adverse pregnancy outcome. METHODS: We enrolled a total of 252 women who had discontinued contraception in order to become pregnant. The first 130 pregnant women were included in the analyses. RESULTS: Multivariate analysis showed a strong association between periodontal disease and adverse pregnancy outcome (OR 5.5, 95% confidence interval 1.4-21.2; p = 0.014), an...

Scale and shape mixtures of multivariate skew-normal distributions

KAUST Repository

Arellano-Valle, Reinaldo B.

2018-02-26

We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.

Derivatives of Multivariate Bernstein Operators and Smoothness with Jacobi Weights

Directory of Open Access Journals (Sweden)

Jianjun Wang

2012-01-01

Full Text Available Using the modulus of smoothness, directional derivatives of multivariate Bernstein operators with weights are characterized. The obtained results partly generalize the corresponding ones for multivariate Bernstein operators without weights.

Multivariate Receptor Models for Spatially Correlated Multipollutant Data

KAUST Repository

Jun, Mikyoung

2013-08-01

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.

Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

Science.gov (United States)

2013-01-01

Background Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG. Methods We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation. Results Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications. Conclusions Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG. PMID:24059247

Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.

Science.gov (United States)

Li, Yanming; Nan, Bin; Zhu, Ji

2015-06-01

We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.

Directional outlyingness for multivariate functional data

KAUST Repository

Dai, Wenlin; Genton, Marc G.

2018-01-01

The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional

Temperature uniformity control in RTP using multivariable adaptive control

Energy Technology Data Exchange (ETDEWEB)

Morales, S.; Dahhou, B.; Dilhac, J.M. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Morales, S.

1995-12-31

In Rapid Thermal Processing (RTP) control of the wafer temperature during all processing to get good trajectory following, together with spatial temperature uniformity, is essential. It is well know as RTP process is nonlinear, classical control laws are not very efficient. In this work, the authors aim at studying the applicability of MIMO (Multiple Inputs Multiple Outputs) adaptive techniques to solve the temperature control problems in RTP. A multivariable linear discrete time CARIMA (Controlled Auto Regressive Integrating Moving Average) model of the highly non-linear process is identified on-line using a robust identification technique. The identified model is used to compute an infinite time LQ (Linear Quadratic) based control law, with a partial state reference model. This reference model smooths the original setpoint sequence, and at the same time gives a tracking capability to the LQ control law. After an experimental open-loop investigation, the results of the application of the adaptive control law are presented. Finally, some comments on the future difficulties and developments of the application of adaptive control in RTP are given. (author) 13 refs.

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

2011-01-01

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...

A Longitudinal Study of Functional Outcomes in Patients with Limb Salvage Surgery for Soft Tissue Sarcoma

Directory of Open Access Journals (Sweden)

Eunsun Oh

2018-01-01

Full Text Available Background. Many studies have reported on the surgical outcomes of soft tissue sarcoma. However, there was no longitudinal cohort study. Because time is the most valuable factor for functional recovery, adjusting time value was the key for finding the causal relationship between other risk factors and postoperative function. Therefore, existing cross-sectional studies can neither fully explain the causal relationship between the risk factors and the functional score nor predict functional recovery. The aim of this study was to determine important predictive factors that affect postoperative functional outcomes and longitudinal changes in functional outcomes in patients who had undergone limb-sparing surgery (LSS for soft tissue sarcoma (STS. Methods. Between January 2008 and December 2014, we retrospectively enrolled 150 patients who had undergone LSS for STS and had been assessed for postoperative functional outcomes with questionnaires. To evaluate functional outcomes, we used the Musculoskeletal Tumor Society (MSTS score and Toronto Extremity Salvage Score (TESS. Multivariate generalized estimating equation (GEE analysis was used to identify the predictive factors, including size, stage, and anatomic location of tumor, bone resection, flap reconstruction, age, and time after surgery. Each continuous variable such as age and time after surgery was explored for statistically significant cutoff points using the Wilcoxon rank sum test. Results. Functional scores significantly improved until the second year after surgery and plateaued for the rest of the 5-year period. Age p<0.0001, bone resection p=0.0004, and time after surgery p<0.0001 were identified as significant predictive factors. The functional score was significantly higher in patients younger than 47 years old. Conclusions. Functional outcomes can improve until the second year after surgery. Patients who were older than 47 and underwent bone resection may have poor final functional

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

Multivariable nonlinear analysis of foreign exchange rates

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2003-05-01

We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.

Multi-variable systems in nuclear power plant

International Nuclear Information System (INIS)

Collins, G.B.; Howell, J.

1982-01-01

Nuclear power plant are complex multi-variable dynamically interactive systems which employ many facets of systems and control theory in their analysis and design. Whole plant mathematical models must be developed and validated and in addition to their obvious role in control system synthesis and design, they are also widely used for operational constraint and plant malfunction analysis. The need for and scope of an integrated power plant control system is discussed and, as a specific example, the design of an integrated feedwater regulator is reviewed. The multi-variable frequency response analysis employed in the design is described in detail. (author)

A "Model" Multivariable Calculus Course.

Science.gov (United States)

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

An Introduction to Applied Multivariate Analysis

CERN Document Server

Raykov, Tenko

2008-01-01

Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

International Nuclear Information System (INIS)

Weathers, J.B.; Luck, R.; Weathers, J.W.

2009-01-01

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

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.

Geographic inequities in liver allograft supply and demand: does it affect patient outcomes?

Science.gov (United States)

Rana, Abbas; Kaplan, Bruce; Riaz, Irbaz B; Porubsky, Marian; Habib, Shahid; Rilo, Horacio; Gruessner, Angelika C; Gruessner, Rainer W G

2015-03-01

Significant geographic inequities mar the distribution of liver allografts for transplantation. We analyzed the effect of geographic inequities on patient outcomes. During our study period (January 1 through December 31, 2010), 11,244 adult candidates were listed for liver transplantation: 5,285 adult liver allografts became available, and 5,471 adult recipients underwent transplantation. We obtained population data from the 2010 United States Census. To determine the effect of regional supply and demand disparities on patient outcomes, we performed linear regression and multivariate Cox regression analyses. Our proposed disparity metric, the ratio of listed candidates to liver allografts available varied from 1.3 (region 11) to 3.4 (region 1). When that ratio was used as the explanatory variable, the R(2) values for outcome measures were as follows: 1-year waitlist mortality, 0.23 and 1-year posttransplant survival, 0.27. According to our multivariate analysis, the ratio of listed candidates to liver allografts available had a significant effect on waitlist survival (hazards ratio, 1.21; 95% confidence interval, 1.04-1.40) but was not a significant risk factor for posttransplant survival. We found significant differences in liver allograft supply and demand--but these differences had only a modest effect on patient outcomes. Redistricting and allocation-sharing schemes should seek to equalize regional supply and demand rather than attempting to equalize patient outcomes.

Children, computer exposure and musculoskeletal outcomes: the development of pathway models for school and home computer-related musculoskeletal outcomes.

Science.gov (United States)

Harris, Courtenay; Straker, Leon; Pollock, Clare; Smith, Anne

2015-01-01

Children's computer use is rapidly growing, together with reports of related musculoskeletal outcomes. Models and theories of adult-related risk factors demonstrate multivariate risk factors associated with computer use. Children's use of computers is different from adult's computer use at work. This study developed and tested a child-specific model demonstrating multivariate relationships between musculoskeletal outcomes, computer exposure and child factors. Using pathway modelling, factors such as gender, age, television exposure, computer anxiety, sustained attention (flow), socio-economic status and somatic complaints (headache and stomach pain) were found to have effects on children's reports of musculoskeletal symptoms. The potential for children's computer exposure to follow a dose-response relationship was also evident. Developing a child-related model can assist in understanding risk factors for children's computer use and support the development of recommendations to encourage children to use this valuable resource in educational, recreational and communication environments in a safe and productive manner. Computer use is an important part of children's school and home life. Application of this developed model, that encapsulates related risk factors, enables practitioners, researchers, teachers and parents to develop strategies that assist young people to use information technology for school, home and leisure in a safe and productive manner.

Burn-center quality improvement: are burn outcomes dependent on admitting facilities and is there a volume-outcome "sweet-spot"?

Science.gov (United States)

Hranjec, Tjasa; Turrentine, Florence E; Stukenborg, George; Young, Jeffrey S; Sawyer, Robert G; Calland, James F

2012-05-01

Risk factors of mortality in burn patients such as inhalation injury, patient age, and percent of total body surface area (%TBSA) burned have been identified in previous publications. However, little is known about the variability of mortality outcomes between burn centers and whether the admitting facilities or facility volumes can be recognized as predictors of mortality. De-identified data from 87,665 acute burn observations obtained from the National Burn Repository between 2003 and 2007 were used to estimate a multivariable logistic regression model that could predict patient mortality with reference to the admitting burn facility/facility volume, adjusted for differences in age, inhalation injury, %TBSA burned, and an additional factor, percent full thickness burn (%FTB). As previously reported, all three covariates (%TBSA burned, inhalation injury, and age) were found to be highly statistically significant risk factors of mortality in burn patients (P value improve the multivariable model. The treatment/admitting facility was found to be an independent mortality predictor, with certain hospitals having increased odds of death and others showing a protective effect (decreased odds ratio). Hospitals with high burn volumes had the highest risk of mortality. Mortality outcomes of patients with similar risk factors (%TBSA burned, inhalation injury, age, and %FTB) are significantly affected by the treating facility and their admission volumes.

Multivariate analytical figures of merit as a metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography-mass spectrometry.

Science.gov (United States)

Eftekhari, Ali; Parastar, Hadi

2016-09-30

The present contribution is devoted to develop multivariate analytical figures of merit (AFOMs) as a new metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). In this regard, new definition of sensitivity (SEN) is extended to GC×GC-MS data and then, other multivariate AFOMs including analytical SEN (γ), selectivity (SEL) and limit of detection (LOD) are calculated. Also, two frequently used second- and third-order calibration algorithms of multivariate curve resolution-alternating least squares (MCR-ALS) as representative of multi-set methods and parallel factor analysis (PARAFAC) as representative of multi-way methods are discussed to exploit pure component profiles and to calculate multivariate AFOMs. Different GC×GC-MS data sets with different number of components along with various levels of artifacts are simulated and analyzed. Noise, elution time shifts in both chromatographic dimensions, peak overlap and interferences are considered as the main artifacts in this work. Additionally, a new strategy is developed to estimate the noise level using variance-covariance matrix of residuals which is very important to calculate multivariate AFOMs. Finally, determination of polycyclic aromatic hydrocarbons (PAHs) in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC-MS is considered as real case to confirm applicability of the proposed metric in real samples. It should be pointed out that the proposed strategy in this work can be used for other types of comprehensive two-dimensional chromatographic (CTDC) techniques like comprehensive two dimensional liquid chromatography (LC×LC). Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of obesity on neonatal outcomes in pregnancies with preterm premature rupture of membranes.

Science.gov (United States)

Faucett, Allison M; Metz, Torri D; DeWitt, Peter E; Gibbs, Ronald S

2016-02-01

Maternal obesity is associated with increased systemic inflammation and an increased risk of preterm premature rupture of membranes. There is an established association between an inflammatory intrauterine environment and adverse neonatal outcomes that is independent of gestational age and mediated by the fetal inflammatory response. It is unknown whether the maternal systemic inflammation that is present in obese women influences the intrauterine environment and predisposes the fetus to adverse neonatal outcomes after preterm premature rupture of membranes. The purpose of this study was to determine whether maternal obesity is associated with adverse neonatal outcomes in pregnancies that are complicated by preterm premature rupture of membranes. This was a secondary analysis of the Maternal-Fetal Medicine Units Network Randomized Clinical Trial on the Beneficial Effects of Antenatal Magnesium Sulfate. Women with singleton pregnancies that were affected by preterm premature rupture of membranes who delivered live-born infants between 24 + 0 and 33 + 6 weeks of gestation were included. An adverse neonatal outcome was defined as a composite outcome of neonatal death, severe necrotizing enterocolitis, respiratory distress syndrome, sepsis, or severe intraventricular hemorrhage. The rates of the composite outcome were compared between obese (body mass index, ≥30 kg/m(2)) and nonobese women. Multivariable logistic regression was used to evaluate the independent effect of obesity on neonatal outcomes. Magnesium sulfate administration, steroid administration, maternal diabetes mellitus, gestational age at delivery, indomethacin exposure, birthweight, and chorioamnionitis were all considered as possible covariates in the multivariable regression models. Three hundred twenty-five of the 1288 women (25.2%) who were included were obese, and 202 of these women (62.2%) had neonates with adverse outcomes. In univariable analysis, maternal prepregnancy obesity was associated

Regression Models For Multivariate Count Data.

Science.gov (United States)

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

The method of separation for evolutionary spectral density estimation of multi-variate and multi-dimensional non-stationary stochastic processes

KAUST Repository

Schillinger, Dominik

2013-07-01

The method of separation can be used as a non-parametric estimation technique, especially suitable for evolutionary spectral density functions of uniformly modulated and strongly narrow-band stochastic processes. The paper at hand provides a consistent derivation of method of separation based spectrum estimation for the general multi-variate and multi-dimensional case. The validity of the method is demonstrated by benchmark tests with uniformly modulated spectra, for which convergence to the analytical solution is demonstrated. The key advantage of the method of separation is the minimization of spectral dispersion due to optimum time- or space-frequency localization. This is illustrated by the calibration of multi-dimensional and multi-variate geometric imperfection models from strongly narrow-band measurements in I-beams and cylindrical shells. Finally, the application of the method of separation based estimates for the stochastic buckling analysis of the example structures is briefly discussed. © 2013 Elsevier Ltd.

High Homocysteine and Blood Pressure Related to Poor Outcome of Acute Ischemia Stroke in Chinese Population

Science.gov (United States)

Liu, Changjiang; Zhao, Liang; Zhou, Mo; Sun, Wenjie; Xu, Tan; Tong, Weijun

2014-01-01

Objectives To assess the association between plasma homocysteine (Hcy), blood pressure (BP) and poor outcome at hospital discharge among acute ischemic stroke patients, and if high Hcy increases the risk of poor outcome based on high BP status in a northern Chinese population. Methods Between June 1, 2009 and May 31, 2013, a total of 3695 acute ischemic stroke patients were recruited from three hospitals in northern Chinese cities. Demographic characteristics, lifestyle risk factors, medical history, and other clinical characteristics were recorded for all subjects. Poor outcome was defined as a discharge modified Rankin Scale (mRS) score ≥3 or death. The association between homocysteine concentration, admission blood pressure, and risk of poor outcome following acute ischemic stroke was analyzed by using multivariate non-conditional logistic regression models. Results Compared with those in the lowest quartile of Hcy concentration in a multivariate-adjusted model, those in the highest quartile of Hcy concentration had increased risk of poor outcome after acute ischemic stroke, (OR = 1.33, P<0.05). The dose-response relationship between Hcy concentration and risk of poor outcome was statistically significant (p-value for trend  = 0.027). High BP was significantly associated with poor outcome following acute ischemic stroke (adjusted OR = 1.44, 95%CI, 1.19–1.74). Compared with non-high BP with nhHcy, in a multivariate-adjusted model, the ORs (95% CI) of non-high BP with hHcy, high BP with nhHcy, and high BP with hHcy to poor outcome were 1.14 (0.85–1.53), 1.37 (1.03–1.84) and 1.70 (1.29–2.34), respectively. Conclusion The present study suggested that high plasma Hcy and blood pressure were independent risk factors for prognosis of acute ischemic stroke, and hHcy may further increase the risk of poor outcome among patients with high blood pressure. Additionally, the results indicate that high Hcy with high BP may cause increased susceptibility

Comparing patients with spinal cord infarction and cerebral infarction: clinical characteristics, and short-term outcome

Directory of Open Access Journals (Sweden)

Naess H

2011-08-01

Full Text Available Halvor Naess, Fredrik RomiDepartment of Neurology, Haukeland University Hospital, N-5021 Bergen, NorwayBackground: To compare the clinical characteristics, and short-term outcome of spinal cord infarction and cerebral infarction.Methods: Risk factors, concomitant diseases, neurological deficits on admission, and short-term outcome were registered among 28 patients with spinal cord infarction and 1075 patients with cerebral infarction admitted to the Department of Neurology, Haukeland University Hospital, Bergen, Norway. Multivariate analyses were performed with location of stroke (cord or brain, neurological deficits on admission, and short-term outcome (both Barthel Index [BI] 1 week after symptom onset and discharge home or to other institution as dependent variables.Results: Multivariate analysis showed that patients with spinal cord infarction were younger, more often female, and less afflicted by hypertension and cardiac disease than patients with cerebral infarction. Functional score (BI was lower among patients with spinal cord infarctions 1 week after onset of symptoms (P < 0.001. Odds ratio for being discharged home was 5.5 for patients with spinal cord infarction compared to cerebral infarction after adjusting for BI scored 1 week after onset (P = 0.019.Conclusion: Patients with spinal cord infarction have a risk factor profile that differs significantly from that of patients with cerebral infarction, although there are some parallels to cerebral infarction caused by atherosclerosis. Patients with spinal cord infarction were more likely to be discharged home when adjusting for early functional level on multivariate analysis.Keywords: spinal cord infarction, cerebral infarction, risk factors, short-term outcome

Development Of A Multivariate Prognostic Model For Pain And Activity Limitation In People With Low Back Disorders Receiving Physiotherapy.

Science.gov (United States)

Ford, Jon J; Richards BPhysio, Matt C; Surkitt BPhysio, Luke D; Chan BPhysio, Alexander Yp; Slater, Sarah L; Taylor, Nicholas F; Hahne, Andrew J

2018-05-28

To identify predictors for back pain, leg pain and activity limitation in patients with early persistent low back disorders. Prospective inception cohort study; Setting: primary care private physiotherapy clinics in Melbourne, Australia. 300 adults aged 18-65 years with low back and/or referred leg pain of ≥6-weeks and ≤6-months duration. Not applicable. Numerical rating scales for back pain and leg pain as well as the Oswestry Disability Scale. Prognostic factors included sociodemographics, treatment related factors, subjective/physical examination, subgrouping factors and standardized questionnaires. Univariate analysis followed by generalized estimating equations were used to develop a multivariate prognostic model for back pain, leg pain and activity limitation. Fifty-eight prognostic factors progressed to the multivariate stage where 15 showed significant (pduration, high multifidus tone, clinically determined inflammation, higher back and leg pain severity, lower lifting capacity, lower work capacity and higher pain drawing percentage coverage). The preliminary model identifying predictors of low back disorders explained up to 37% of the variance in outcome. This study evaluated a comprehensive range of prognostic factors reflective of both the biomedical and psychosocial domains of low back disorders. The preliminary multivariate model requires further validation before being considered for clinical use. Copyright © 2018. Published by Elsevier Inc.

Reported Prestroke Physical Activity Is Associated with Vascular Endothelial Growth Factor Expression and Good Outcomes after Stroke.

Science.gov (United States)

López-Cancio, Elena; Ricciardi, Ana Clara; Sobrino, Tomás; Cortés, Jordi; de la Ossa, Natalia Pérez; Millán, Mónica; Hernández-Pérez, María; Gomis, Meritxell; Dorado, Laura; Muñoz-Narbona, Lucía; Campos, Francisco; Arenillas, Juan F; Dávalos, Antoni

2017-02-01

Physical activity (PhA) prior to stroke has been associated with good outcomes after the ischemic insult, but there is scarce data on the involved molecular mechanisms. We studied consecutive acute ischemic stroke patients admitted to a single tertiary stroke center. Prestroke PhA was evaluated with the International Physical Activity Questionnaire (metabolic equivalent of minutes/week). We studied several circulating angiogenic and neurogenic factors at different time points: vascular endothelial growth factor (VEGF), granulocyte colony-stimulating factor (G-CSF), and brain-derived neurotrophic factor (BDNF) at admission, day 7, and at 3 months. We considered good functional outcome at 3 months (modified Rankin scale  ≤ 2) as primary end point, and final infarct volume as secondary outcome. We studied 83 patients with at least 2 time point serum determinations (mean age 69.6 years, median National Institutes of Health Stroke Scale 17 at admission). Patients more physically active before stroke had a significantly higher increment of serum VEGF on the seventh day when compared to less active patients. This increment was an independent predictor of good functional outcome at 3 months and was associated with smaller infarct volume in multivariate analyses adjusted for relevant covariates. We did not find independent associations of G-CSF or BDNF levels neither with level of prestroke PhA nor with stroke outcomes. Although there are probably more molecular mechanisms by which PhA exerts its beneficial effects in stroke outcomes, our observation regarding the potential role of VEGF is plausible and in line with previous experimental studies. Further research in this field is needed. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Predictors of Positive Outcomes in Offspring of Depressed Parents and Non-depressed Parents Across 20 Years

Science.gov (United States)

Verdeli, Helen; Wickramaratne, Priya; Warner, Virginia; Mancini, Anthony; Weissman, Myrna

2014-01-01

Understanding differences in factors leading to positive outcomes in high-risk and low-risk offspring has important implications for preventive interventions. We identified variables predicting positive outcomes in a cohort of 235 offspring from 76 families in which one, both, or neither parent had major depressive disorder. Positive outcomes were termed resilient in offspring of depressed parents, and competent in offspring of non-depressed parents, and defined by two separate criteria: absence of psychiatric diagnosis and consistently high functioning at 2, 10, and 20 years follow-up. In offspring of depressed parents, easier temperament and higher self-esteem were associated with greater odds of resilient outcome defined by absence of diagnosis. Lower maternal overprotection, greater offspring self-esteem, and higher IQ were associated with greater odds of resilient outcome defined by consistently high functioning. Multivariate analysis indicated that resilient outcome defined by absence of diagnosis was best predicted by offspring self-esteem; resilient outcome defined by functioning was best predicted by maternal overprotection and self-esteem. Among offspring of non-depressed parents, greater family cohesion, easier temperament and higher self-esteem were associated with greater odds of offspring competent outcome defined by absence of diagnosis. Higher maternal affection and greater offspring self-esteem were associated with greater odds of competent outcome, defined by consistently high functioning. Multivariate analysis for each criterion indicated that competent outcome was best predicted by offspring self-esteem. As the most robust predictor of positive outcomes in offspring of depressed and non-depressed parents, self-esteem is an important target for youth preventive interventions. PMID:25374449

EXPLORATORY DATA ANALYSIS AND MULTIVARIATE STRATEGIES FOR REVEALING MULTIVARIATE STRUCTURES IN CLIMATE DATA

Directory of Open Access Journals (Sweden)

2016-12-01

Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.

MULTIVARIATERESIDUES : A Mathematica package for computing multivariate residues

Science.gov (United States)

Larsen, Kasper J.; Rietkerk, Robbert

2018-01-01

Multivariate residues appear in many different contexts in theoretical physics and algebraic geometry. In theoretical physics, they for example give the proper definition of generalized-unitarity cuts, and they play a central role in the Grassmannian formulation of the S-matrix by Arkani-Hamed et al. In realistic cases their evaluation can be non-trivial. In this paper we provide a Mathematica package for efficient evaluation of multivariate residues based on methods from computational algebraic geometry.

Multivariate methods and forecasting with IBM SPSS statistics

CERN Document Server

Aljandali, Abdulkader

2017-01-01

This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...

The covariance between the number of accidents and the number of victims in multivariate analysis of accident related outcomes

NARCIS (Netherlands)

Bijleveld, F. D.

In this study some statistical issues involved in the simultaneous analysis of accident related outcomes of the road traffic process are investigated. Since accident related outcomes like the number of victims, fatalities or accidents show interdependencies, their simultaneous analysis requires that

Exploring the potential of multivariate depth-damage and rainfall-damage models

DEFF Research Database (Denmark)

van Ootegem, Luc; van Herck, K.; Creten, T.

2018-01-01

In Europe, floods are among the natural catastrophes that cause the largest economic damage. This article explores the potential of two distinct types of multivariate flood damage models: ‘depth-damage’ models and ‘rainfall-damage’ models. We use survey data of 346 Flemish households that were...... victim of pluvial floods complemented with rainfall data from both rain gauges and weather radars. In the econometrical analysis, a Tobit estimation technique is used to deal with the issue of zero damage observations. The results show that in the ‘depth-damage’ models flood depth has a significant...... impact on the damage. In the ‘rainfall-damage’ models there is a significant impact of rainfall accumulation on the damage when using the gauge rainfall data as predictor, but not when using the radar rainfall data. Finally, non-hazard indicators are found to be important for explaining pluvial flood...

Periodontal Disease and Bacterial Vaginosis Increase the Risk for Adverse Pregnancy Outcome

OpenAIRE

Oittinen, Juha; Kurki, Tapio; Kekki, Minnamaija; Kuusisto, Minna; Pussinen, Pirkko; Vilkuna-Rautiainen, Tiina; Nieminen, Anja; Asikainen, Sirkka; Paavonen, Jorma

2005-01-01

Objectives. To determine whether periodontal disease or bacterial vaginosis (BV) diagnosed before pregnancy increase the risk for adverse pregnancy outcome.Methods. We enrolled a total of 252 women who had discontinued contraception in order to become pregnant. The first 130 pregnant women were included in the analyses.Results. Multivariate analysis showed a strong association between periodontal disease and adverse pregnancy outcome (OR 5.5, 95% confidence interval 1.4–21.2; p = 0.014), and ...

Influences on call outcomes among Veteran callers to the National Veterans Crisis Line

Science.gov (United States)

Britton, Peter C.; Bossarte, Robert M.; Thompson, Caitlin; Kemp, Janet; Conner, Kenneth R.

2016-01-01

This evaluation examined the association of caller and call characteristics with proximal outcomes of Veterans Crisis Line calls. From October 1-7, 2010, 665 Veterans with recent suicidal ideation or a history of attempted suicide called the Veterans Crisis Line, 646 had complete data and were included in the analyses. A multivariable multinomial logistic regression was conducted to identify correlates of a favorable outcome, either a resolution or a referral, when compared to an unfavorable outcome, no resolution or referral. A multivariable logistic regression was used to identify correlates of responder-rated caller risk in a subset of calls. Approximately 84% of calls ended with a favorable outcome, 25% with a resolution and 59% with a referral to a local health care provider. Calls from high-risk callers had greater odds of ending with a referral than without a resolution or referral, as did weekday calls (6:00 am to 5:59 pm EST, Monday through Friday). Responders used caller intent to die and the absence of future plans to determine caller risk. Findings suggest that the Veterans Crisis Line is a useful mechanism for generating referrals for high-risk Veteran callers. Responders appeared to use known risk and protective factors to determine caller risk. PMID:23611446

Association of pretreatment neutrophil-lymphocyte ratio and outcome in emergency colorectal cancer care.

Science.gov (United States)

Palin, R P; Devine, A T; Hicks, G; Burke, D

2018-04-01

Introduction The association between the neutrophil-lymphocyte ratio (NLR) and outcome in elective colorectal cancer surgery is well established; the relationship between NLR and the emergency colorectal cancer patient is, as yet, unexplored. This paper evaluates the predictive quality of the NLR for outcome in the emergency colorectal cancer patient. Materials and Methods A total of 187 consecutive patients who underwent emergency surgery for colorectal cancer were included in the study. NLR was calculated from the haematological tests done on admission. Receiver operating characteristic analyses were used to determine the most suitable cut-off for NLR. Outcomes were assessed by mortality at 30 and 90 days using stepwise Cox proportional hazards regression. Results An NLR cut-off of 5 was found to have the highest sensitivity and specificity. At 30 days, age and time from admission to surgery were associated with increased mortality; a high NLR was associated with an increased risk of mortality in univariate but not multivariate analysis. At 90 days, age, NLR, time from admission to surgery and nodal status were all significantly associated with increased mortality on multivariate analysis. Conclusions Pre-operative NLR is a cheap, easily performed and useful clinical tool to aid prediction of outcome in the emergency colorectal cancer patient.

Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

Energy Technology Data Exchange (ETDEWEB)

Kolluri, Srinivas Sahan; Esfahani, Iman Janghorban; Garikiparthy, Prithvi Sai Nadh; Yoo, Chang Kyoo [Kyung Hee University, Yongin (Korea, Republic of)

2015-08-15

Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

International Nuclear Information System (INIS)

Kolluri, Srinivas Sahan; Esfahani, Iman Janghorban; Garikiparthy, Prithvi Sai Nadh; Yoo, Chang Kyoo

2015-01-01

Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

Integrated Employee Occupational Health and Organizational-Level Registered Nurse Outcomes.

Science.gov (United States)

Mohr, David C; Schult, Tamara; Eaton, Jennifer Lipkowitz; Awosika, Ebi; McPhaul, Kathleen M

2016-05-01

The study examined organizational culture, structural supports, and employee health program integration influence on registered nurse (RN) outcomes. An organizational health survey, employee health clinical operations survey, employee attitudes survey, and administration data were collected. Multivariate regression models examined outcomes of sick leave, leave without pay, voluntary turnover, intention to leave, and organizational culture using 122 medical centers. Lower staffing ratios were associated with greater sick leave, higher turnover, and intention to leave. Safety climate was favorably associated with each of the five outcomes. Both onsite employee occupational health services and a robust health promotion program were associated with more positive organizational culture perceptions. Findings highlight the positive influence of integrating employee health and health promotion services on organizational health outcomes. Attention to promoting employee health may benefit organizations in multiple, synergistic ways.

Bayesian Inference of a Multivariate Regression Model

Directory of Open Access Journals (Sweden)

Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

Mulch materials in processing tomato: a multivariate approach

Directory of Open Access Journals (Sweden)

Marta María Moreno

2013-08-01

Full Text Available Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L. crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2, one oxo-biodegradable material (OB, two types of paper (PP1, PP2, and one barley straw cover (BS were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]. A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.

Poor oral status is associated with rehabilitation outcome in older people.

Science.gov (United States)

Shiraishi, Ai; Yoshimura, Yoshihiro; Wakabayashi, Hidetaka; Tsuji, Yuri

2017-04-01

Poor oral status is associated with increased physical dependency and cognitive decline. Malnutrition, a potential result of poor oral status, is associated with poorer rehabilitation outcome and physical function. However, the association between oral status and rehabilitation outcome is not fully understood. The present study investigated the association of poor oral status with rehabilitation outcome in older patients. A retrospective cohort study was carried out of 108 consecutive patients (mean age 80.5 ± 6.8 years; 50.9% men) who were admitted to convalescent rehabilitation wards. The Revised Oral Assessment Guide was used to evaluate oral status. Rehabilitation outcome was evaluated by the Functional Independence Measure (FIM) on discharge. Multivariate analyses were applied to examine the associations between poor oral status and motor-FIM on discharge. According to the Revised Oral Assessment Guide score, 14.8% of participants had normal oral status, 52.8% had slight to moderate oral problems and 32.4% had severe oral problems. The median scores of motor-FIM on admission and on discharge were 52 (interquartile range 25-70) and 75 (interquartile range 51-89), respectively. Multivariate analysis showed that the Revised Oral Assessment Guide score and the motor-/cognitive-FIM scores on admission were significant independent factors for motor-FIM on discharge, after adjusted for sex, age, length of stay, nutritional status, handgrip and causative diseases (P < 0.001). Poor oral status is associated with rehabilitation outcome in older people. Geriatr Gerontol Int 2017; 17: 598-604. © 2016 Japan Geriatrics Society.

Neonatal Pulmonary MRI of Bronchopulmonary Dysplasia Predicts Short-term Clinical Outcomes.

Science.gov (United States)

Higano, Nara S; Spielberg, David R; Fleck, Robert J; Schapiro, Andrew H; Walkup, Laura L; Hahn, Andrew D; Tkach, Jean A; Kingma, Paul S; Merhar, Stephanie L; Fain, Sean B; Woods, Jason C

2018-05-23

Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current NICHD/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. Forty-two neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD controls; 40±3 weeks post-menstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo-time and gradient echo) in a NICU-sited, neonatal-sized 1.5T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared to clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at NICU discharge (P<0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this non-ionizing technique can be implemented to phenotype disease and has potential to serially assess efficacy of individualized therapies.

Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective

International Nuclear Information System (INIS)

Peng, Weiwen; Li, Yan-Feng; Mi, Jinhua; Yu, Le; Huang, Hong-Zhong

2016-01-01

Degradation analysis is critical to reliability assessment and operational management of complex systems. Two types of assumptions are often adopted for degradation analysis: (1) single degradation indicator and (2) constant external factors. However, modern complex systems are generally characterized as multiple functional and suffered from multiple failure modes due to dynamic operating conditions. In this paper, Bayesian degradation analysis of complex systems with multiple degradation indicators under dynamic conditions is investigated. Three practical engineering-driven issues are addressed: (1) to model various combinations of degradation indicators, a generalized multivariate hybrid degradation process model is proposed, which subsumes both monotonic and non-monotonic degradation processes models as special cases, (2) to study effects of external factors, two types of dynamic covariates are incorporated jointly, which include both environmental conditions and operating profiles, and (3) to facilitate degradation based reliability analysis, a serial of Bayesian strategy is constructed, which covers parameter estimation, factor-related degradation prediction, and unit-specific remaining useful life assessment. Finally, degradation analysis of a type of heavy machine tools is presented to demonstrate the application and performance of the proposed method. A comparison of the proposed model with a traditional model is studied as well in the example. - Highlights: • A generalized multivariate hybrid degradation process model is introduced. • Various types of dependent degradation processes can be modeled coherently. • The effects of environmental conditions and operating profiles are investigated. • Unit-specific RUL assessment is implemented through a two-step Bayesian method.

The analysis of multivariate group differences using common principal components

NARCIS (Netherlands)

Bechger, T.M.; Blanca, M.J.; Maris, G.

2014-01-01

Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

Simulations of full multivariate Tweedie with flexible dependence structure

DEFF Research Database (Denmark)

Cuenin, Johann; Jørgensen, Bent; Kokonendji, Célestin C.

2016-01-01

The paper introduces a variables-in-common method for constructing and simulating multivariate Tweedie distribution, based on linear combinations of independent univariate Tweedie variables. The method is facilitated by the convolution and scaling properties of the Tweedie distributions, using....... The method allows simulation of multivariate distributions from many known, including the Gaussian, Poisson, non-central gamma, gamma and inverse Gaussian distributions....

Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution

KAUST Repository

Ahmed, Qasim Zeeshan

2015-04-01

Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.

Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling

Directory of Open Access Journals (Sweden)

Jens G. Balchen

1995-04-01

Full Text Available A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END. Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables. No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC with very convincing results.

External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

Science.gov (United States)

2014-01-01

Background Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models. Methods We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures. Results 11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models. Conclusions The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling

Multivariate log-skew-elliptical distributions with applications to precipitation data

KAUST Repository

Marchenko, Yulia V.

2009-07-13

We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.

Multivariate log-skew-elliptical distributions with applications to precipitation data

KAUST Repository

Marchenko, Yulia V.; Genton, Marc G.

2009-01-01

We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.

Association Between Physician Teamwork and Health System Outcomes After Coronary Artery Bypass Grafting.

Science.gov (United States)

Hollingsworth, John M; Funk, Russell J; Garrison, Spencer A; Owen-Smith, Jason; Kaufman, Samuel A; Pagani, Francis D; Nallamothu, Brahmajee K

2016-11-01

Patients undergoing coronary artery bypass grafting (CABG) must often see multiple providers dispersed across many care locations. To test whether teamwork (assessed with the bipartite clustering coefficient) among these physicians is a determinant of surgical outcomes, we examined national Medicare data from patients undergoing CABG. Among Medicare beneficiaries who underwent CABG between 2008 and 2011, we mapped relationships between all physicians who treated them during their surgical episodes, including both surgeons and nonsurgeons. After aggregating across CABG episodes in a year to construct the physician social networks serving each health system, we then assessed the level of physician teamwork in these networks with the bipartite clustering coefficient. Finally, we fit a series of multivariable regression models to evaluate associations between a health system's teamwork level and its 60-day surgical outcomes. We observed substantial variation in the level of teamwork between health systems performing CABG (SD for the bipartite clustering coefficient was 0.09). Although health systems with high and low teamwork levels treated beneficiaries with comparable comorbidity scores, these health systems differed over several sociocultural and healthcare capacity factors (eg, physician staff size and surgical caseload). After controlling for these differences, health systems with higher teamwork levels had significantly lower 60-day rates of emergency department visit, readmission, and mortality. Health systems with physicians who tend to work together in tightly-knit groups during CABG episodes realize better surgical outcomes. As such, delivery system reforms focused on building teamwork may have positive effects on surgical care. © 2016 American Heart Association, Inc.

Association Between Physician Teamwork and Health System Outcomes Following Coronary Artery Bypass Grafting

Science.gov (United States)

Hollingsworth, John M.; Funk, Russell J.; Garrison, Spencer A.; Owen-Smith, Jason; Kaufman, Samuel A.; Pagani, Francis D.; Nallamothu, Brahmajee K.

2017-01-01

Background Patients undergoing coronary artery bypass grafting (CABG) must often see multiple providers dispersed across many care locations. To test whether “teamwork” (assessed with the bipartite clustering coefficient) among these physicians is a determinant of surgical outcomes, we examined national Medicare data from patients undergoing CABG. Methods and Results Among Medicare beneficiaries who underwent CABG between 2008 and 2011, we mapped relationships between all physicians who treated them during their surgical episodes, including both surgeons and nonsurgeons. After aggregating across CABG episodes in a year to construct the physician social networks serving each health system, we then assessed the level of physician teamwork in these networks with the bipartite clustering coefficient. Finally, we fit a series of multivariable regression models to evaluate associations between a health system’s teamwork level and its 60-day surgical outcomes. We observed substantial variation in the level of teamwork between health systems performing CABG (standard deviation for the bipartite clustering coefficient was 0.09). While health systems with high and low teamwork levels treated beneficiaries with comparable comorbidity scores, these health systems differed over several sociocultural and healthcare capacity factors (e.g., physician staff size, surgical caseload). After controlling for these differences, health systems with higher teamwork levels had significantly lower 60-day rates of emergency department visit, readmission, and mortality. Conclusions Health systems with physicians who tend to work together in tightly knit groups during CABG episodes realize better surgical outcomes. As such, delivery system reforms focused on building teamwork may have positive effects on surgical care. PMID:28263939

Forecasting multivariate volatility in larger dimensions: some practical issues

OpenAIRE

Adam E Clements; Ayesha Scott; Annastiina Silvennoinen

2012-01-01

The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techn...

Synthetic environmental indicators: A conceptual approach from the multivariate statistics

International Nuclear Information System (INIS)

Escobar J, Luis A

2008-01-01

This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.

Determinants of outcomes in patients with simple gastroschisis.

Science.gov (United States)

Youssef, Fouad; Laberge, Jean-Martin; Puligandla, Pramod; Emil, Sherif

2017-05-01

We analyzed the determinants of outcomes in simple gastroschisis (GS) not complicated by intestinal atresia, perforation, or necrosis. All simple GS patients enrolled in a national prospective registry from 2005 to 2013 were studied. Patients below the median for total parenteral nutrition (TPN) duration (26days) and hospital stay (34days) were compared to those above. Univariate and multivariate logistic and linear regression analyses were employed using maternal, patient, postnatal, and treatment variables. Of 700 patients with simple GS, representing 76.8% of all GS patients, 690 (98.6%) survived. TPN was used in 352 (51.6%) and 330 (48.4%) patients for ≤26 and >26days, respectively. Hospital stay for 356 (51.9%) and 330 (48.1%) infants was ≤34 and >34days, respectively. Univariate analysis revealed significant differences in several patient, treatment, and postnatal factors. On multivariate analysis, prenatal sonographic bowel dilation, older age at closure, necrotizing enterocolitis, longer mechanical ventilation, and central-line associated blood stream infection (CLABSI) were independently associated with longer TPN duration and hospital stay, with CLABSI being the strongest predictor. Prenatal bowel dilation is associated with increased morbidity in simple GS. CLABSI is the strongest predictor of outcomes. Bowel matting is not an independent risk factor. 2c. Copyright © 2017 Elsevier Inc. All rights reserved.

Relationship between chest compression rates and outcomes from cardiac arrest.

Science.gov (United States)

Idris, Ahamed H; Guffey, Danielle; Aufderheide, Tom P; Brown, Siobhan; Morrison, Laurie J; Nichols, Patrick; Powell, Judy; Daya, Mohamud; Bigham, Blair L; Atkins, Dianne L; Berg, Robert; Davis, Dan; Stiell, Ian; Sopko, George; Nichol, Graham

2012-06-19

Guidelines for cardiopulmonary resuscitation recommend a chest compression rate of at least 100 compressions per minute. Animal and human studies have reported that blood flow is greatest with chest compression rates near 120/min, but few have reported rates used during out-of-hospital (OOH) cardiopulmonary resuscitation or the relationship between rate and outcome. The purpose of this study was to describe chest compression rates used by emergency medical services providers to resuscitate patients with OOH cardiac arrest and to determine the relationship between chest compression rate and outcome. Included were patients aged ≥ 20 years with OOH cardiac arrest treated by emergency medical services providers participating in the Resuscitation Outcomes Consortium. Data were abstracted from monitor-defibrillator recordings during cardiopulmonary resuscitation. Multiple logistic regression analysis assessed the association between chest compression rate and outcome. From December 2005 to May 2007, 3098 patients with OOH cardiac arrest were included in this study. Mean age was 67 ± 16 years, and 8.6% survived to hospital discharge. Mean compression rate was 112 ± 19/min. A curvilinear association between chest compression rate and return of spontaneous circulation was found in cubic spline models after multivariable adjustment (P=0.012). Return of spontaneous circulation rates peaked at a compression rate of ≈ 125/min and then declined. Chest compression rate was not significantly associated with survival to hospital discharge in multivariable categorical or cubic spline models. Chest compression rate was associated with return of spontaneous circulation but not with survival to hospital discharge in OOH cardiac arrest.

Correlation between smoking habit and surgical outcomes on viral-associated hepatocellular carcinomas.

Science.gov (United States)

Kai, Keita; Komukai, Sho; Koga, Hiroki; Yamaji, Koutaro; Ide, Takao; Kawaguchi, Atsushi; Aishima, Shinichi; Noshiro, Hirokazu

2018-01-07

To investigate the association between smoking habits and surgical outcomes in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) (B-HCC) and hepatitis C virus (HCV)-related HCC (C-HCC) and clarify the clinicopathological features associated with smoking status in B-HCC and C-HCC patients. We retrospectively examined the cases of the 341 consecutive patients with viral-associated HCC (C-HCC, n = 273; B-HCC, n = 68) who underwent curative surgery for their primary lesion. We categorized smoking status at the time of surgery into never, ex- and current smoker. We analyzed the B-HCC and C-HCC groups' clinicopathological features and surgical outcomes, i.e ., disease-free survival (DFS), overall survival (OS), and disease-specific survival (DSS). Univariate and multivariate analyses were performed using a Cox proportional hazards regression model. We also performed subset analyses in both patient groups comparing the current smokers to the other patients. The multivariate analysis in the C-HCC group revealed that current-smoker status was significantly correlated with both OS ( P = 0.0039) and DSS ( P = 0.0416). In the B-HCC patients, no significant correlation was observed between current-smoker status and DFS, OS, or DSS in the univariate or multivariate analyses. The subset analyses comparing the current smokers to the other patients in both the C-HCC and B-HCC groups revealed that the current smokers developed HCC at significantly younger ages than the other patients irrespective of viral infection status. A smoking habit is significantly correlated with the overall and disease-specific survivals of patients with C-HCC. In contrast, the B-HCC patients showed a weak association between smoking status and surgical outcomes.

Robust methods for multivariate data analysis A1

DEFF Research Database (Denmark)

Frosch, Stina; Von Frese, J.; Bro, Rasmus

2005-01-01

Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...

Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...

Learning multivariate distributions by competitive assembly of marginals.

Science.gov (United States)

Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

2013-02-01

We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

In-hospital outcome in patients with ST elevation myocardial infarction and right bundle branch block. A sub-study from RENASICA II, a national multicenter registry.

Science.gov (United States)

Juárez-Herrera, Ursulo; Jerjes Sánchez, Carlos; González-Pacheco, Héctor; Martínez-Sánchez, Carlos

2010-01-01

Compare in-hospital outcome in patients with ST-elevation myocardial infarction with right versus left bundle branch block. RENASICA II, a national Mexican registry enrolled 8098 patients with final diagnosis of acute coronary syndrome secondary to ischemic heart disease. In 4555 STEMI patients, 545 had bundle branch block, 318 (58.3%) with right and 225 patients with left (41.6%). Both groups were compared in terms of in-hospital outcome through major cardiovascular adverse events; (cardiovascular death, recurrent ischemia and reinfarction). Multivariable analysis was performed to identify in-hospital mortality risk among right and left bundle branch block patients. There were not statistical differences in both groups regarding baseline characteristics, time of ischemia, myocardial infarction location, ventricular dysfunction and reperfusion strategies. In-hospital outcome in bundle branch block group was characterized by a high incidence of major cardiovascular adverse events with a trend to higher mortality in patients with right bundle branch block (OR 1.70, CI 1.19 - 2.42, p right bundle branch block accompanying ST-elevation myocardial infarction of any location at emergency room presentation was an independent predictor of high in-hospital mortality.

A Range-Based Multivariate Model for Exchange Rate Volatility

OpenAIRE

Tims, Ben; Mahieu, Ronald

2003-01-01

textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...

Multivariate Variables Recognition using Hotelling’s T2 and MEWMA via ANN’s

Directory of Open Access Journals (Sweden)

Chiñas-Sánchez Pamela

2014-01-01

Full Text Available In this article, a method for multivariate pattern recognition using artificial neural networks (ANN is proposed. The method is useful for monitoring multiple variables during the statistical process control. It employs descriptive statistics and multivariate control techniques. Three different ANN’s are evaluated to identify the network with higher efficiency during pattern recognition of multivariate variables tasks from data bases. Two data bases are analyzed; the first one is generated by simulation using the Montecarlo method, and the second data base was obtained from a public data base repository. The method consists of three stages: multivariate variables generation, multivariate analysis and pattern recognition using ANN’s. Several multivariate scenarios were generated using a combination of 2, 3 and 4 patterns in multivariate variables for the Hotelling’s T2 and MEWMA statistics that were analyzed to know its behavior and to determine their statistical characteristics. The pattern recognition task was evaluated using the ANN. In both study cases, experimental results showed an improved efficiency when using the Perceptron and the Backpropagation networks compared to the RBF network.

Implementation Challenges for Multivariable Control: What You Did Not Learn in School

Science.gov (United States)

Garg, Sanjay

2008-01-01

Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

SPICE: exploration and analysis of post-cytometric complex multivariate datasets.

Science.gov (United States)

Roederer, Mario; Nozzi, Joshua L; Nason, Martha C

2011-02-01

Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. Published 2011 Wiley-Liss, Inc.

Multivariate Location Estimation Using Extension of $R$-Estimates Through $U$-Statistics Type Approach

OpenAIRE

Chaudhuri, Probal

1992-01-01

We consider a class of $U$-statistics type estimates for multivariate location. The estimates extend some $R$-estimates to multivariate data. In particular, the class of estimates includes the multivariate median considered by Gini and Galvani (1929) and Haldane (1948) and a multivariate extension of the well-known Hodges-Lehmann (1963) estimate. We explore large sample behavior of these estimates by deriving a Bahadur type representation for them. In the process of developing these asymptoti...

Estimating the decomposition of predictive information in multivariate systems

Science.gov (United States)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Black Hole Sign Predicts Poor Outcome in Patients with Intracerebral Hemorrhage.

Science.gov (United States)

Li, Qi; Yang, Wen-Song; Chen, Sheng-Li; Lv, Fu-Rong; Lv, Fa-Jin; Hu, Xi; Zhu, Dan; Cao, Du; Wang, Xing-Chen; Li, Rui; Yuan, Liang; Qin, Xin-Yue; Xie, Peng

2018-01-01

In spontaneous intracerebral hemorrhage (ICH), black hole sign has been proposed as a promising imaging marker that predicts hematoma expansion in patients with ICH. The aim of our study was to investigate whether admission CT black hole sign predicts hematoma growth in patients with ICH. From July 2011 till February 2016, patients with spontaneous ICH who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan were recruited into the study. The presence of black hole sign on admission non-enhanced CT was independently assessed by 2 readers. The functional outcome was assessed using the modified Rankin Scale (mRS) at 90 days. Univariate and multivariable logistic regression analyses were performed to assess the association between the presence of the black hole sign and functional outcome. A total of 225 patients (67.6% male, mean age 60.3 years) were included in our study. Black hole sign was identified in 32 of 225 (14.2%) patients on admission CT scan. The multivariate logistic regression analysis demonstrated that age, intraventricular hemorrhage, baseline ICH volume, admission Glasgow Coma Scale score, and presence of black hole sign on baseline CT independently predict poor functional outcome at 90 days. There are significantly more patients with a poor functional outcome (defined as mRS ≥4) among patients with black hole sign than those without (84.4 vs. 32.1%, p black hole sign independently predicts poor outcome in patients with ICH. Early identification of black hole sign is useful in prognostic stratification and may serve as a potential therapeutic target for anti-expansion clinical trials. © 2018 S. Karger AG, Basel.

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

2017-08-10

A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

Science.gov (United States)

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Fully conditional specification in multivariate imputation

NARCIS (Netherlands)

van Buuren, S.; Brand, J. P.L.; Groothuis-Oudshoorn, C. G.M.; Rubin, D. B.

2006-01-01

The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the

A note on inconsistent families of discrete multivariate distributions

KAUST Repository

Ghosh, Sugata; Dutta, Subhajit; Genton, Marc G.

2017-01-01

We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.

A note on inconsistent families of discrete multivariate distributions

KAUST Repository

Ghosh, Sugata

2017-07-05

We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.

Multivariate Term Structure Models with Level and Heteroskedasticity Effects

DEFF Research Database (Denmark)

Christiansen, Charlotte

2005-01-01

The paper introduces and estimates a multivariate level-GARCH model for the long rate and the term-structure spread where the conditional volatility is proportional to the ãth power of the variable itself (level effects) and the conditional covariance matrix evolves according to a multivariate GA...... and the level model. GARCH effects are more important than level effects. The results are robust to the maturity of the interest rates. Udgivelsesdato: MAY...

On set-valued functionals: Multivariate risk measures and Aumann integrals

Science.gov (United States)

Ararat, Cagin

In this dissertation, multivariate risk measures for random vectors and Aumann integrals of set-valued functions are studied. Both are set-valued functionals with values in a complete lattice of subsets of Rm. Multivariate risk measures are considered in a general d-asset financial market with trading opportunities in discrete time. Specifically, the following features of the market are incorporated in the evaluation of multivariate risk: convex transaction costs modeled by solvency regions, intermediate trading constraints modeled by convex random sets, and the requirement of liquidation into the first m ≤ d of the assets. It is assumed that the investor has a "pure" multivariate risk measure R on the space of m-dimensional random vectors which represents her risk attitude towards the assets but does not take into account the frictions of the market. Then, the investor with a d-dimensional position minimizes the set-valued functional R over all m-dimensional positions that she can reach by trading in the market subject to the frictions described above. The resulting functional Rmar on the space of d-dimensional random vectors is another multivariate risk measure, called the market-extension of R. A dual representation for R mar that decomposes the effects of R and the frictions of the market is proved. Next, multivariate risk measures are studied in a utility-based framework. It is assumed that the investor has a complete risk preference towards each individual asset, which can be represented by a von Neumann-Morgenstern utility function. Then, an incomplete preference is considered for multivariate positions which is represented by the vector of the individual utility functions. Under this structure, multivariate shortfall and divergence risk measures are defined as the optimal values of set minimization problems. The dual relationship between the two classes of multivariate risk measures is constructed via a recent Lagrange duality for set optimization. In

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

Science.gov (United States)

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

Directory of Open Access Journals (Sweden)

Binod Neupane

Full Text Available In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when

Multivariable Super Twisting Based Robust Trajectory Tracking Control for Small Unmanned Helicopter

Directory of Open Access Journals (Sweden)

Xing Fang

2015-01-01

Full Text Available This paper presents a highly robust trajectory tracking controller for small unmanned helicopter with model uncertainties and external disturbances. First, a simplified dynamic model is developed, where the model uncertainties and external disturbances are treated as compounded disturbances. Then the system is divided into three interconnected subsystems: altitude subsystem, yaw subsystem, and horizontal subsystem. Second, a disturbance observer based controller (DOBC is designed based upon backstepping and multivariable super twisting control algorithm to obtain robust trajectory tracking property. A sliding mode observer works as an estimator of the compounded disturbances. In order to lessen calculative burden, a first-order exact differentiator is employed to estimate the time derivative of the virtual control. Moreover, proof of the stability of the closed-loop system based on Lyapunov method is given. Finally, simulation results are presented to illustrate the effectiveness and robustness of the proposed flight control scheme.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit; Genton, Marc G.

2017-01-01

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit

2017-04-05

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Operative volume and outcomes of cerebrovascular neurosurgery in children.

Science.gov (United States)

Bekelis, Kimon; Connolly, Ian D; Do, Huy M; Choudhri, Omar

2016-11-01

OBJECTIVE The impact of procedural volume on the outcomes of cerebrovascular surgery in children has not been determined. In this study, the authors investigated the association of operative volume on the outcomes of cerebrovascular neurosurgery in pediatric patients. METHODS The authors performed a cohort study of all pediatric patients who underwent a cerebrovascular procedure between 2003 and 2012 and were registered in the Kids' Inpatient Database (KID). To control for confounding, the authors used multivariable regression models, propensity-score conditioning, and mixed-effects analysis to account for clustering at the hospital level. RESULTS During the study period, 1875 pediatric patients in the KID underwent cerebrovascular neurosurgery and met the inclusion criteria for the study; 204 patients (10.9%) underwent aneurysm clipping, 446 (23.8%) underwent coil insertion for an aneurysm, 827 (44.1%) underwent craniotomy for arteriovenous malformation resection, and 398 (21.2%) underwent bypass surgery for moyamoya disease. Mixed-effects multivariable regression analysis revealed that higher procedural volume was associated with fewer inpatient deaths (OR 0.58; 95% CI 0.40-0.85), a lower rate of discharges to a facility (OR 0.87; 95% CI 0.82-0.92), and shorter length of stay (adjusted difference -0.22; 95% CI -0.32 to -0.12). The results in propensity-adjusted multivariable models were robust. CONCLUSIONS In a national all-payer cohort of pediatric patients who underwent a cerebrovascular procedure, the authors found that higher procedural volume was associated with fewer deaths, a lower rate of discharges to a facility, and decreased lengths of stay. Regionalization initiatives should include directing children with such rare pathologies to a center of excellence.

Fourier expansions and multivariable Bessel functions concerning radiation programmes

International Nuclear Information System (INIS)

Dattoli, G.; Richetta, M.; Torre, A.; Chiccoli, C.; Lorenzutta, S.; Maino, G.

1996-01-01

The link between generalized Bessel functions and other special functions is investigated using the Fourier series and the generalized Jacobi-Anger expansion. A new class of multivariable Hermite polynomials is then introduced and their relevance to physical problems discussed. As an example of the power of the method, applied to radiation physics, we analyse the role played by multi-variable Bessel functions in the description of radiation emitted by a charge constrained to a nonlinear oscillation. (author)

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

Science.gov (United States)

Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix

2014-02-15

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward

Influence of Salmonella status on the long-term outcome of horses after colic surgery.

Science.gov (United States)

Southwood, Louise L; Lindborg, Susan; Myers, Marc; Aceto, Helen W

2017-08-01

To compare long-term outcome of Salmonella-positive versus Salmonella-negative horses discharged from hospital after colic surgery. Retrospective case-control. Horses discharged from the hospital after colic surgery. For each horse with positive culture for Salmonella enterica (SAL-POS, n = 59), at least 2 horses testing negative for S. enterica (SAL-NEG, n = 119) were enrolled. Owners were interviewed via phone at least 12 months after surgery regarding: (1) complications after discharge from the hospital; (2) duration of survival; and (3) return to prior or intended use. Association between immediate postoperative clinical variables such as Salmonella status and long-term measures of outcome was tested via ratios (odds ratio [OR]) and 95% confidence intervals. Data were analyzed for survival using a Cox proportional hazards model and for return to use using multivariable logistic regression. SAL-POS horses had a higher OR of surgical site infection (2.7 [1.1-6.9] P = .027) and weight loss (6.8 [1.8-26.1] P = .002). At the time of follow-up, there were 53/56 (95%) SAL-POS and 99/118 (84%) SAL-NEG horses alive. The final multivariable model for nonsurvival included postoperative colic (hazard ratio 7.6 [2.8-19.2] P = .002) and the interaction between Salmonella status and duration of rectal temperature > 103°F postoperatively (SAL-POS 1.04 [1.01-1.07] and SAL-NEG 1.16 [1.06-1.25], P = .005). The majority of horses returned to their intended use regardless of their SAL-POS (38/50, 76%) or SAL-NEG (77/96, 80%, P = .498) status. Salmonella-positive horses that survive to discharge from the hospital after colic surgery have similar risks of long-term complications (colic/diarrhea), survival, and return to function than Salmonella-negative horses. © 2017 The American College of Veterinary Surgeons.

Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response.

Science.gov (United States)

Binder, Harald; Sauerbrei, Willi; Royston, Patrick

2013-06-15

In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2)  = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.

The Effect of the Multivariate Box-Cox Transformation on the Power of MANOVA.

Science.gov (United States)

Kirisci, Levent; Hsu, Tse-Chi

Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…

A Multivariate Analysis of Personality, Values and Expectations as Correlates of Career Aspirations of Final Year Medical Students

Science.gov (United States)

Rogers, Mary E.; Searle, Judy; Creed, Peter A.; Ng, Shu-Kay

2010-01-01

This study reports on the career intentions of 179 final year medical students who completed an online survey that included measures of personality, values, professional and lifestyle expectations, and well-being. Logistic regression analyses identified the determinants of preferred medical specialty, practice location and hours of work.…

Processing data collected from radiometric experiments by multivariate technique

International Nuclear Information System (INIS)

Urbanski, P.; Kowalska, E.; Machaj, B.; Jakowiuk, A.

2005-01-01

Multivariate techniques applied for processing data collected from radiometric experiments can provide more efficient extraction of the information contained in the spectra. Several techniques are considered: (i) multivariate calibration using Partial Least Square Regression and Artificial Neural Network, (ii) standardization of the spectra, (iii) smoothing of collected spectra were autocorrelation function and bootstrap were used for the assessment of the processed data, (iv) image processing using Principal Component Analysis. Application of these techniques is illustrated on examples of some industrial applications. (author)

Prestroke physical activity is associated with good functional outcome and arterial recanalization after stroke due to a large vessel occlusion.

Science.gov (United States)

Ricciardi, Ana Clara; López-Cancio, Elena; Pérez de la Ossa, Natalia; Sobrino, Tomás; Hernández-Pérez, María; Gomis, Meritxell; Munuera, Josep; Muñoz, Lucía; Dorado, Laura; Millán, Mónica; Dávalos, Antonio; Arenillas, Juan F

2014-01-01

Although multiple studies and meta-analyses have consistently suggested that regular physical activity (PhA) is associated with a decreased stroke risk and recurrence, there is limited data on the possible preconditioning effect of prestroke PhA on stroke severity and prognosis. We aimed to study the association of prestroke PhA with different outcome variables in patients with acute ischemic stroke due to an anterior large vessel occlusion. The Prestroke Physical Activity and Functional Recovery in Patients with Ischemic Stroke and Arterial Occlusion trial is an observational and longitudinal study that included consecutive patients with acute ischemic stroke admitted to a single tertiary stroke center. Main inclusion criteria were: anterior circulation ischemic stroke within 12 h from symptom onset; presence of a confirmed anterior large vessel occlusion, and functional independence previous to stroke. Prestroke PhA was evaluated with the International Physical Activity Questionnaire and categorized into mild, moderate and high levels by means of metabolic equivalent (MET) minutes per week thresholds. The primary outcome measure was good functional outcome at 3 months (modified Rankin scale ≤2). Secondary outcomes were severity of stroke at admission, complete early recanalization, early dramatic neurological improvement and final infarct volume. During the study period, 159 patients fulfilled the above criteria. The mean age was 68 years, 62% were men and the baseline NIHSS score was 17. Patients with high levels of prestroke PhA were younger, had more frequently distal occlusions and had lower levels of blood glucose and fibrinogen at admission. After multivariate analysis, a high level of prestroke PhA was associated with a good functional outcome at 3 months. Regarding secondary outcome variables and after adjustment for relevant factors, a high level of prestroke PhA was independently associated with milder stroke severity at admission, early dramatic

Weak convergence of marked point processes generated by crossings of multivariate jump processes

DEFF Research Database (Denmark)

Tamborrino, Massimiliano; Sacerdote, Laura; Jacobsen, Martin

2014-01-01

We consider the multivariate point process determined by the crossing times of the components of a multivariate jump process through a multivariate boundary, assuming to reset each component to an initial value after its boundary crossing. We prove that this point process converges weakly...... process converging to a multivariate Ornstein–Uhlenbeck process is discussed as a guideline for applying diffusion limits for jump processes. We apply our theoretical findings to neural network modeling. The proposed model gives a mathematical foundation to the generalization of the class of Leaky...

Multivariate analysis for customer segmentation based on RFM

Directory of Open Access Journals (Sweden)

Álvaro Julio Cuadros López

2018-02-01

Full Text Available Context: To build a successful relationship management (CRM, companies must start with the identification of the true value of customers, as this provides basic information to implement more targeted and customized marketing strategies. The RFM methodology, a classic analysis tool that uses three evaluation parameters, allows companies to understand customer behavior, and to establish customer segments. The addition of a new parameter in the traditional technique is an opportunity to refine the possible outcomes of a customer segmentation since it not only provides a new element of evaluation to identify the most valuable customers, but it also makes it possible to differentiate and get to know customers even better. Method: The article presents a methodology that allows to establish customer segments using an extended RFM method with new variables, selected through multivariate analysis..  Results: The proposed implementation was applied in a company in which variables such as profit, profit percentage, and billing due date were tested. Therefore, it was possible to establish a more detailed customer segmentation than with the classic RFM. Conclusions: the RFM analysis is a method widely used in the industry for its easy understanding and applicability. However, it can be improved with the use of statistical procedures and new variables, which will allow companies to have deeper information about the behavior of the clients, and will facilitate the design of specific marketing strategies.

The Japanese Histologic Classification and T-score in the Oxford Classification system could predict renal outcome in Japanese IgA nephropathy patients.

Science.gov (United States)

Kaihan, Ahmad Baseer; Yasuda, Yoshinari; Katsuno, Takayuki; Kato, Sawako; Imaizumi, Takahiro; Ozeki, Takaya; Hishida, Manabu; Nagata, Takanobu; Ando, Masahiko; Tsuboi, Naotake; Maruyama, Shoichi

2017-12-01

The Oxford Classification is utilized globally, but has not been fully validated. In this study, we conducted a comparative analysis between the Oxford Classification and Japanese Histologic Classification (JHC) to predict renal outcome in Japanese patients with IgA nephropathy (IgAN). A retrospective cohort study including 86 adult IgAN patients was conducted. The Oxford Classification and the JHC were evaluated by 7 independent specialists. The JHC, MEST score in the Oxford Classification, and crescents were analyzed in association with renal outcome, defined as a 50% increase in serum creatinine. In multivariate analysis without the JHC, only the T score was significantly associated with renal outcome. While, a significant association was revealed only in the JHC on multivariate analysis with JHC. The JHC and T score in the Oxford Classification were associated with renal outcome among Japanese patients with IgAN. Superiority of the JHC as a predictive index should be validated with larger study population and cohort studies in different ethnicities.

Comparison of multivariate and univariate statistical process control and monitoring methods

International Nuclear Information System (INIS)

Leger, R.P.; Garland, WM.J.; Macgregor, J.F.

1996-01-01

Work in recent years has lead to the development of multivariate process monitoring schemes which use Principal Component Analysis (PCA). This research compares the performance of a univariate scheme and a multivariate PCA scheme used for monitoring a simple process with 11 measured variables. The multivariate PCA scheme was able to adequately represent the process using two principal components. This resulted in a PCA monitoring scheme which used two charts as opposed to 11 charts for the univariate scheme and therefore had distinct advantages in terms of both data representation, presentation, and fault diagnosis capabilities. (author)

Time varying, multivariate volume data reduction

Energy Technology Data Exchange (ETDEWEB)

Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS

2010-01-01

Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

A Spatially Constrained Multi-autoencoder Approach for Multivariate Geochemical Anomaly Recognition

Science.gov (United States)

Lirong, C.; Qingfeng, G.; Renguang, Z.; Yihui, X.

2017-12-01

Separating and recognizing geochemical anomalies from the geochemical background is one of the key tasks in geochemical exploration. Many methods have been developed, such as calculating the mean ±2 standard deviation, and fractal/multifractal models. In recent years, deep autoencoder, a deep learning approach, have been used for multivariate geochemical anomaly recognition. While being able to deal with the non-normal distributions of geochemical concentrations and the non-linear relationships among them, this self-supervised learning method does not take into account the spatial heterogeneity of geochemical background and the uncertainty induced by the randomly initialized weights of neurons, leading to ineffective recognition of weak anomalies. In this paper, we introduce a spatially constrained multi-autoencoder (SCMA) approach for multivariate geochemical anomaly recognition, which includes two steps: spatial partitioning and anomaly score computation. The first step divides the study area into multiple sub-regions to segregate the geochemical background, by grouping the geochemical samples through K-means clustering, spatial filtering, and spatial constraining rules. In the second step, for each sub-region, a group of autoencoder neural networks are constructed with an identical structure but different initial weights on neurons. Each autoencoder is trained using the geochemical samples within the corresponding sub-region to learn the sub-regional geochemical background. The best autoencoder of a group is chosen as the final model for the corresponding sub-region. The anomaly score at each location can then be calculated as the euclidean distance between the observed concentrations and reconstructed concentrations of geochemical elements.The experiments using the geochemical data and Fe deposits in the southwestern Fujian province of China showed that our SCMA approach greatly improved the recognition of weak anomalies, achieving the AUC of 0.89, compared

Automated computer-based CT stratification as a predictor of outcome in hypersensitivity pneumonitis

International Nuclear Information System (INIS)

Jacob, Joseph; Mak, S.M.; Mok, W.; Hansell, D.M.; Bartholmai, B.J.; Rajagopalan, S.; Karwoski, R.; Della Casa, G.; Sugino, K.; Walsh, S.L.F.; Wells, A.U.

2017-01-01

Hypersensitivity pneumonitis (HP) has a variable clinical course. Modelling of quantitative CALIPER-derived CT data can identify distinct disease phenotypes. Mortality prediction using CALIPER analysis was compared to the interstitial lung disease gender, age, physiology (ILD-GAP) outcome model. CALIPER CT analysis of parenchymal patterns in 98 consecutive HP patients was compared to visual CT scoring by two radiologists. Functional indices including forced vital capacity (FVC) and diffusion capacity for carbon monoxide (DLco) in univariate and multivariate Cox mortality models. Automated stratification of CALIPER scores was evaluated against outcome models. Univariate predictors of mortality included visual and CALIPER CT fibrotic patterns, and all functional indices. Multivariate analyses identified only two independent predictors of mortality: CALIPER reticular pattern (p = 0.001) and DLco (p < 0.0001). Automated stratification distinguished three distinct HP groups (log-rank test p < 0.0001). Substitution of automated stratified groups for FVC and DLco in the ILD-GAP model demonstrated no loss of model strength (C-Index = 0.73 for both models). Model strength improved when automated stratified groups were combined with the ILD-GAP model (C-Index = 0.77). CALIPER-derived variables are the strongest CT predictors of mortality in HP. Automated CT stratification is equivalent to functional indices in the ILD-GAP model for predicting outcome in HP. (orig.)

Automated computer-based CT stratification as a predictor of outcome in hypersensitivity pneumonitis

Energy Technology Data Exchange (ETDEWEB)

Jacob, Joseph; Mak, S.M.; Mok, W.; Hansell, D.M. [Royal Brompton and Harefield NHS Foundation Trust, Department of Radiology, Royal Brompton Hospital, London (United Kingdom); Bartholmai, B.J. [Mayo Clinic Rochester, Division of Radiology, Rochester, MN (United States); Rajagopalan, S.; Karwoski, R. [Mayo Clinic Rochester, Biomedical Imaging Resource, Rochester, MN (United States); Della Casa, G. [Universita degli Studi di Modena e Reggio Emilia, Modena, Emilia-Romagna (Italy); Sugino, K. [Toho University Omori Medical Centre, Tokyo (Japan); Walsh, S.L.F. [Kings College Hospital, London (United Kingdom); Wells, A.U. [Royal Brompton and Harefield NHS Foundation Trust, Interstitial Lung Disease Unit, Royal Brompton Hospital, London (United Kingdom)

2017-09-15

Hypersensitivity pneumonitis (HP) has a variable clinical course. Modelling of quantitative CALIPER-derived CT data can identify distinct disease phenotypes. Mortality prediction using CALIPER analysis was compared to the interstitial lung disease gender, age, physiology (ILD-GAP) outcome model. CALIPER CT analysis of parenchymal patterns in 98 consecutive HP patients was compared to visual CT scoring by two radiologists. Functional indices including forced vital capacity (FVC) and diffusion capacity for carbon monoxide (DLco) in univariate and multivariate Cox mortality models. Automated stratification of CALIPER scores was evaluated against outcome models. Univariate predictors of mortality included visual and CALIPER CT fibrotic patterns, and all functional indices. Multivariate analyses identified only two independent predictors of mortality: CALIPER reticular pattern (p = 0.001) and DLco (p < 0.0001). Automated stratification distinguished three distinct HP groups (log-rank test p < 0.0001). Substitution of automated stratified groups for FVC and DLco in the ILD-GAP model demonstrated no loss of model strength (C-Index = 0.73 for both models). Model strength improved when automated stratified groups were combined with the ILD-GAP model (C-Index = 0.77). CALIPER-derived variables are the strongest CT predictors of mortality in HP. Automated CT stratification is equivalent to functional indices in the ILD-GAP model for predicting outcome in HP. (orig.)

Low vascularity predicts favourable outcomes in leiomyoma patients treated with uterine artery embolization.

Science.gov (United States)

Tang, Yixin; Chen, Chunlin; Duan, Hui; Ma, Ben; Liu, Ping

2016-10-01

To investigate the clinical factors predicting outcomes of leiomyoma treated with uterine artery embolization (UAE). A total of 183 uterine leiomyoma patients undergoing UAE were retrospectively analyzed. Patient age, characteristics of vascular supply in magnetic resonance imaging (MRI)/digital subtraction angiography (DSA), number, size and location of leiomyoma were recorded. Leiomyoma regrowth, new leiomyoma appearance and recurrence of any previously reported symptoms were carefully monitored over a mean follow-up of 30 months (median 32 months, range 12-80). Potential recurrence risk factors were analyzed by univariate and multivariate cox regression analysis. Twenty-three recurrences were recorded. The difference in the vascularity classification systems between MRI and DSA was not statistically significant (P = 0.059). High vascularity in MRI, high vascularity in DSA and multiple leiomyoma showed a significant risk of recurrence using univariate and multivariate analysis (P = 0.004, P leiomyoma recurrence (P > 0.05). Low vascularity and solitary leiomyoma indicated favourable outcomes in patients treated with UAE. • Low vascularity and solitary mass predicted favourable outcomes in UAE-treated patients. • MRI might provide information on vascularity in leiomyoma before UAE. • Variations in vascular supply, age, size, location were not associated with recurrence.

Boosted Multivariate Trees for Longitudinal Data

Science.gov (United States)

Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant

2017-01-01

Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for longitudinal data. In this model, features are assumed to be nonparametric, while feature-time interactions are modeled semi-nonparametrically utilizing P-splines with estimated smoothing parameter. In order to avoid overfitting, we describe a relatively simple in sample cross-validation method which can be used to estimate the optimal boosting iteration and which has the surprising added benefit of stabilizing certain parameter estimates. Our new multivariate tree boosting method is shown to be highly flexible, robust to covariance misspecification and unbalanced designs, and resistant to overfitting in high dimensions. Feature selection can be used to identify important features and feature-time interactions. An application to longitudinal data of forced 1-second lung expiratory volume (FEV1) for lung transplant patients identifies an important feature-time interaction and illustrates the ease with which our method can find complex relationships in longitudinal data. PMID:29249866

Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research.

Science.gov (United States)

Graffelman, Jan; van Eeuwijk, Fred

2005-12-01

The scatter plot is a well known and easily applicable graphical tool to explore relationships between two quantitative variables. For the exploration of relations between multiple variables, generalisations of the scatter plot are useful. We present an overview of multivariate scatter plots focussing on the following situations. Firstly, we look at a scatter plot for portraying relations between quantitative variables within one data matrix. Secondly, we discuss a similar plot for the case of qualitative variables. Thirdly, we describe scatter plots for the relationships between two sets of variables where we focus on correlations. Finally, we treat plots of the relationships between multiple response and predictor variables, focussing on the matrix of regression coefficients. We will present both known and new results, where an important original contribution concerns a procedure for the inclusion of scales for the variables in multivariate scatter plots. We provide software for drawing such scales. We illustrate the construction and interpretation of the plots by means of examples on data collected in a genomic research program on taste in tomato.

Assessment of Student Professional Outcomes for Continuous Improvement

Science.gov (United States)

Keshavarz, Mohsen; Baghdarnia, Mostafa

2013-01-01

This article describes a method for the assessment of professional student outcomes (performance-type outcomes or soft skills). The method is based upon group activities, research on modern electrical engineering topics by individual students, classroom presentations on chosen research topics, final presentations, and technical report writing.…

Multivariate Fréchet copulas and conditional value-at-risk

Directory of Open Access Journals (Sweden)

Werner Hürlimann

2004-01-01

is similar but not identical to the convex family of Fréchet. It is shown that the distribution and stop-loss transform of dependent sums from this multivariate family can be evaluated using explicit integral formulas, and that these dependent sums are bounded in convex order between the corresponding independent and comonotone sums. The model is applied to the evaluation of the economic risk capital for a portfolio of risks using conditional value-at-risk measures. A multivariate conditional value-at-risk vector measure is considered. Its components coincide for the constructed multivariate copula with the conditional value-at-risk measures of the risk components of the portfolio. This yields a “fair” risk allocation in the sense that each risk component becomes allocated to its coherent conditional value-at-risk.

TMVA - Toolkit for Multivariate Data Analysis with ROOT Users guide

CERN Document Server

Höcker, A; Tegenfeldt, F; Voss, H; Voss, K; Christov, A; Henrot-Versillé, S; Jachowski, M; Krasznahorkay, A; Mahalalel, Y; Prudent, X; Speckmayer, P

2007-01-01

Multivariate machine learning techniques for the classification of data from high-energy physics (HEP) experiments have become standard tools in most HEP analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a toolkit integrated in ROOT which hosts a large variety of multivariate classification algorithms. They range from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA organises the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to the analysis of data sets with unknown sample composition.

Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level

International Nuclear Information System (INIS)

Tunga, M. Alper; Demiralp, Metin

2011-01-01

Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.

Male gender and renal dysfunction are predictors of adverse outcome in nonpostoperative ischemic colitis patients.

Science.gov (United States)

Lee, Tsung-Chun; Wang, Hsiu-Po; Chiu, Han-Mo; Lien, Wan-Ching; Chen, Mei-Jyh; Yu, Linda C H; Sun, Chia-Tung; Lin, Jaw-Town; Wu, Ming-Shiang

2010-01-01

Ischemic colitis (IC) spans a broad spectrum from self-limiting illness to intestinal gangrene and mortality. Prognostic factors specifically for nonpostoperative IC were not fully characterized. We aim to focus on nonpostoperative IC in patients with renal dysfunction and try to identify prognostic factors for adverse outcomes. We conducted a retrospective analysis at a university-affiliated tertiary medical center in Taiwan. From January 2003 to August 2008, 25 men and 52 women (mean age: 66 y) had colonoscopic biopsy-proven IC without prior culprit surgery. We estimated glomerular filtration rate with simplified Modification of Diet in Renal Disease equation. Nine patients with glomerular filtration rate below 30 mL per minute per 1.73 m were classified as renal dysfunction group (including 7 dialysis patients). Adverse outcomes were defined as need for surgery and mortality. Predictors for adverse outcomes were captured by univariate and multivariate analysis. Research ethical committee approved the study protocol. Patients with renal dysfunction more often had: diabetes mellitus (56% vs. 16%, P=0.02), prolonged symptoms (6.8 d vs. 3.5 d, P=0.01), lower hemoglobin (11.1 g/dL vs. 13.4 g/dL, P=0.01), and more often right colonic involvement (56% vs. 19%, P=0.03). Renal dysfunction patients also had longer hospitalization days (median 15 d vs. 4 d, P=0.045). However, there was no statistical significance in the rate of either surgery or mortality between these 2 groups (P>0.05). Univariate analysis showed that renal dysfunction, sex, emergency department referral, presentation with abdominal pain were significant for adverse outcome (P<0.1). Multivariate analysis revealed that male sex conveyed 9.5-fold risk (P=0.01) and renal dysfunction conveyed 8.5-fold risk (P=0.03) for adverse outcomes. Nonpostoperative IC patients with concurrent renal dysfunction had distinct clinical profiles. Multivariate analysis showed that male patients had 9.5-fold and renal

Collision prediction models using multivariate Poisson-lognormal regression.

Science.gov (United States)

El-Basyouny, Karim; Sayed, Tarek

2009-07-01

This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.

A direct-gradient multivariate index of biotic condition

Science.gov (United States)

Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

2012-01-01

Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

Bone marrow MR imaging as predictors of outcome in hemopoietic stem cell transplantation

Energy Technology Data Exchange (ETDEWEB)

Shen, Jun; Cheng, Li-Na; Duan, Xiao-Hui; Liang, Bi-Ling [Sun Yat-sen University, Department of Radiology, Guangzhou, Guangdong (China); Second Affiliated Hospital, Guangzhou, Guangdong (China); Griffith, James F. [Chinese University of Hong Kong, Prince of Wales Hospital, Department of Diagnostic Radiology and Organ Imaging, Shatin, Hong Kong SAR (China); Xu, Hong-Gui [Sun Yat-sen University, Department of Pediatrics, Guangzhou, Guangdong (China); Second Affiliated Hospital, Guangzhou, Guangdong (China)

2008-09-15

The purpose of this study is to investigate the role of femoral marrow MR imaging as predictor of outcome for hemopoietic stem cell transplantation (HSCT) in beta-thalassemia major. MR imaging of the proximal femur, including T1- and T2-weighted spin echo and short-tau inversion recovery and in-phase and out-of-phase fast field echo images, was prospectively performed in 27 thalassemia major patients being prepared for HSCT. The area of red marrow and its percentage of the proximal femur were measured, and the presence of marrow hemosiderosis was assessed. Age-adjusted multivariate logistic regression was used to determine the relationship between red marrow area percentage and marrow hemosiderosis and HSCT outcome. Red area percentage were less in patients with successful (90.25{+-}4.14%) compared to unsuccessful transplants (94.54% {+-}2.93%; p=0.01). Red marrow area percentage correlated positively with duration of symptoms(r=0.428, p=0.026) and serum ferritin (r=0.511, p=0.006). In multivariate-adjusted logistic regression analyses, red marrow area percentage was significantly inversely associated with successful HSCT (OR=1.383, 95% CI: 1.059-1.805, p=0.005). Marrow hemosidersosis and duration of sympotms and serum ferritin were not associated with HSCT outcome(p=0.174, 0.974, 0.762, respectively). Red marrow area percentage of proximal femur on MR imaging is a useful predictor of HSCT outcome. (orig.)

Bone marrow MR imaging as predictors of outcome in hemopoietic stem cell transplantation

International Nuclear Information System (INIS)

Shen, Jun; Cheng, Li-Na; Duan, Xiao-Hui; Liang, Bi-Ling; Griffith, James F.; Xu, Hong-Gui

2008-01-01

The purpose of this study is to investigate the role of femoral marrow MR imaging as predictor of outcome for hemopoietic stem cell transplantation (HSCT) in beta-thalassemia major. MR imaging of the proximal femur, including T1- and T2-weighted spin echo and short-tau inversion recovery and in-phase and out-of-phase fast field echo images, was prospectively performed in 27 thalassemia major patients being prepared for HSCT. The area of red marrow and its percentage of the proximal femur were measured, and the presence of marrow hemosiderosis was assessed. Age-adjusted multivariate logistic regression was used to determine the relationship between red marrow area percentage and marrow hemosiderosis and HSCT outcome. Red area percentage were less in patients with successful (90.25±4.14%) compared to unsuccessful transplants (94.54% ±2.93%; p=0.01). Red marrow area percentage correlated positively with duration of symptoms(r=0.428, p=0.026) and serum ferritin (r=0.511, p=0.006). In multivariate-adjusted logistic regression analyses, red marrow area percentage was significantly inversely associated with successful HSCT (OR=1.383, 95% CI: 1.059-1.805, p=0.005). Marrow hemosidersosis and duration of sympotms and serum ferritin were not associated with HSCT outcome(p=0.174, 0.974, 0.762, respectively). Red marrow area percentage of proximal femur on MR imaging is a useful predictor of HSCT outcome. (orig.)

Rethinking the dose-response relationship between usage and outcome in an online intervention for depression: randomized controlled trial.

Science.gov (United States)

Donkin, Liesje; Hickie, Ian B; Christensen, Helen; Naismith, Sharon L; Neal, Bruce; Cockayne, Nicole L; Glozier, Nick

2013-10-17

program. In a multivariate regression model, only the number of activities completed per log-in was associated with a clinically significant outcome (OR 2.82, 95% CI 1.05-7.59). The final model predicted 7.4% of variance in outcome. Curve estimates indicated that significant logarithmic (P=.009) and linear (P=.002) relationships existed between activities completed per log-in and clinically significant change. Only one objective measure of usage was independently associated with better outcome of a Web-based intervention of known effectiveness. The 4 usage metrics retained in the final step of the regression accounted for little outcome variance. Medium level users appeared to have little additional benefit compared to low users indicating that assumptions of a linear relationship between use and outcome may be too simplistic and further models and variables need to be explored to adequately understand the relationship.

Outcomes of a contemporary cohort of 536 consecutive patients with acute ischemic stroke treated with endovascular therapy.

Science.gov (United States)

Abilleira, Sònia; Cardona, Pere; Ribó, Marc; Millán, Mònica; Obach, Víctor; Roquer, Jaume; Cánovas, David; Martí-Fàbregas, Joan; Rubio, Francisco; Alvarez-Sabín, José; Dávalos, Antoni; Chamorro, Angel; de Miquel, Maria Angeles; Tomasello, Alejandro; Castaño, Carlos; Macho, Juan M; Ribera, Aida; Gallofré, Miquel

2014-04-01

We sought to assess outcomes after endovascular treatment/therapy of acute ischemic stroke, overall and by subgroups, and looked for predictors of outcome. We used data from a mandatory, population-based registry that includes external monitoring of completeness, which assesses reperfusion therapies for consecutive patients with acute ischemic stroke since 2011. We described outcomes overall and by subgroups (age ≤ or >80 years; onset-to-groin puncture ≤ or >6 hours; anterior or posterior strokes; previous IV recombinant tissue-type plasminogen activator or isolated endovascular treatment/therapy; revascularization or no revascularization), and determined independent predictors of good outcome (modified Rankin Scale score ≤2) and mortality at 3 months by multivariate modeling. We analyzed 536 patients, of whom 285 received previous IV recombinant tissue-type plasminogen activator. Overall, revascularization (modified Thrombolysis In Cerebral Infarction scores, 2b and 3) occurred in 73.9%, 5.6% developed symptomatic intracerebral hemorrhages, 43.3% achieved good functional outcome, and 22.2% were dead at 90 days. Adjusted comparisons by subgroups systematically favored revascularization (lower proportion of symptomatic intracerebral hemorrhages and death rates and higher proportion of good outcome). Multivariate analyses confirmed the independent protective effect of revascularization. Additionally, age >80 years, stroke severity, hypertension (deleterious), atrial fibrillation, and onset-to-groin puncture ≤6 hours (protective) also predicted good outcome, whereas lack of previous disability and anterior circulation strokes (protective) as well as and hypertension (deleterious) independently predicted mortality. This study reinforces the role of revascularization and time to treatment to achieve enhanced functional outcomes and identifies other clinical features that independently predict good/fatal outcome after endovascular treatment/therapy.

Simplicial band depth for multivariate functional data

KAUST Repository

Ló pez-Pintado, Sara; Sun, Ying; Lin, Juan K.; Genton, Marc G.

2014-01-01

sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation

Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

Science.gov (United States)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.

Associations between blood glucose level and outcomes of adult in-hospital cardiac arrest: a retrospective cohort study.

Science.gov (United States)

Wang, Chih-Hung; Huang, Chien-Hua; Chang, Wei-Tien; Tsai, Min-Shan; Yu, Ping-Hsun; Wu, Yen-Wen; Chen, Wen-Jone

2016-08-24

We intended to analyse the associations between blood glucose (BG) level and clinical outcomes of in-hospital cardiac arrest (IHCA). We conducted a retrospective observational study in a single medical centre and evaluated patients who experienced IHCA between 2006 and 2014. We used multivariable logistic regression analysis to study associations between independent variables and outcomes. We calculated the mean BG level for each patient by averaging the maximum and minimum BG levels in the first 24 h after arrest, and we used mean BG level for our final analysis. We included a total of 402 patients. Of these, 157 patients (39.1 %) had diabetes mellitus (DM). The average mean BG level was 209.9 mg/dL (11.7 mmol/L). For DM patients, a mean BG level between 183 and 307 mg/dL (10.2-17.1 mmol/L) was significantly associated with favourable neurological outcome (odds ratio [OR] 2.71, 95 % confidence interval [CI] 1.18-6.20; p value = 0.02); a mean BG level between 147 and 317 mg/dL (8.2-17.6 mmol/L) was significantly associated with survival to hospital discharge (OR 2.38, 95 % CI 1.26-4.53; p value = 0.008). For non-DM patients, a mean BG level between 143 and 268 mg/dL (7.9-14.9 mmol/L) was significantly associated with survival to hospital discharge (OR 2.93, 95 % CI 1.62-5.40; p value level in the first 24 h after cardiac arrest was associated with neurological outcome for IHCA patients with DM. For neurological and survival outcomes, the optimal BG range may be higher for patients with DM than for patients without DM.

Clinical Characteristics and Predictors of Outcome for Onconeural Antibody-Associated Disorders: A Retrospective Analysis

Directory of Open Access Journals (Sweden)

Shaohua Liao

2017-11-01

Full Text Available ObjectiveTo describe and analyze the clinical characteristics, laboratory data, management, and outcome of patients with onconeural antibody-associated disorders (OAAD and identify predictors for poor outcome.MethodsThis was a retrospective review of all patients with potential OAAD, who were hospitalized in Jinan General Hospital between September 2009 and July 2017. We clarified the diagnosis, collected comprehensive information and categorized patients into three groups: paraneoplastic neurological disorders (PNDs, autoimmune encephalitis (AE, and possible OAAD. Within the three groups, we analyzed a range of clinical and laboratory parameters and used univariate and multivariate regression analysis to identify predictors for poor outcome [modified Rankin Scale (mRS = 3–6].ResultsFrom 158 patients, we identified 70 who fulfilled the criteria for OAAD, including 44 men (62.9% and 26 women (37.1%. There were 38 patients (54.3% in the PNDs group, 14 patients (20% in the AE group, and 18 patients (25.7% in the possible OAAD group. After the last follow-up, 14 (36.8%, 9 (64.2%, and 12 (66.7% had a good outcome (mRS = 0–2. However, 6 (15.8%, 2 (14.3%, and 3 (16.7% died, respectively. Univariate analysis showed that duration prior to the hospital (p = 0.0224 and urinary incontinence/retention (p = 0.0043 were associated with poor outcome (mRS = 3–6. After multivariate regression analysis, urinary incontinence/retention (p = 0.0388 and an immunocompromised state (p = 0.0247 remained as significant factors for poor outcome.ConclusionUrinary incontinence/retention and an immunocompromised state represent significant predictors of a worse prognosis for patients with OAAD. By contrast, cerebrospinal fluid analysis showed that serum autoantibodies and tumor markers, the function of crucial organs, electrophysiology, and radiological findings were not associated with a poor outcome.

Pre-Pregnancy Dating Violence and Birth Outcomes Among Adolescent Mothers in a National Sample.

Science.gov (United States)

Madkour, Aubrey Spriggs; Xie, Yiqiong; Harville, Emily W

2014-07-01

Although infants born to adolescent mothers are at increased risk of adverse birth outcomes, little is known about contributors to birth outcomes in this group. Given past research linking partner abuse to adverse birth outcomes among adult mothers, we explored associations between pre-pregnancy verbal and physical dating violence and the birth weight and gestational age of infants born to adolescent mothers. Data from the National Longitudinal Study of Adolescent Health Waves I (1995/1996), II (1996), and IV (2007/2008) were analyzed. Girls whose first singleton live births occurred after Wave II interview and before age 20 (N = 558) self-reported infants' birth weight and gestational age at Wave IV. Dating violence victimization (verbal and physical) in the 18 months prior to Wave II interview was self-reported. Controls included Wave I age, parent education, age at pregnancy, time between reporting abuse and birth, and childhood physical and sexual abuse. Weighted multivariable regression models were performed separately by race (Black/non-Black).On average, births occurred 2 years after Wave II interview. Almost one in four mothers reported verbal dating violence victimization (23.6%), and 10.1% reported physical victimization. Birth weight and prevalence of verbal dating violence victimization were significantly lower in Black compared with non-Black teen mothers. In multivariable analyses, negative associations between physical dating abuse and birth outcomes became stronger as time increased for Black mothers. For example, pre-pregnancy physical dating abuse was associated with 0.79 kilograms lower birth weight (pdating abuse was unassociated with birth outcomes among non-Black mothers, and verbal abuse was unassociated with birth outcomes for all mothers. Reducing physical dating violence in adolescent relationships prior to pregnancy may improve Black adolescent mothers' birth outcomes. Intervening on long-term violence may be particularly important. �

Effect of Preoperative Fatty Degeneration of the Rotator Cuff Muscles on the Clinical Outcome of Patients With Intact Tendons After Arthroscopic Rotator Cuff Repair of Large/Massive Cuff Tears.

Science.gov (United States)

Ohzono, Hiroki; Gotoh, Masafumi; Nakamura, Hidehiro; Honda, Hirokazu; Mitsui, Yasuhiro; Kakuma, Tatsuyuki; Okawa, Takahiro; Shiba, Naoto

2017-11-01

Fatty degeneration of the rotator cuff muscles is associated not only with postoperative retear but also with postoperative muscle weakness; therefore, fatty changes in the muscles may affect the clinical outcome even in patients with these tears who have intact tendons after arthroscopic rotator cuff repair (ARCR). To evaluate the effect of fatty infiltration on the clinical outcome in patients with intact tendons after arthroscopic repair of large/massive cuff tears. Case-control study; Level of evidence, 3. One hundred fifty-five consecutive patients with large/massive rotator cuff tears underwent ARCR. Of these, 55 patients (mean ± SD age, 64.4 ± 9.1 years) in whom intact tendons after surgery were confirmed with magnetic resonance imaging at final follow-up (mean ± SD, 2.5 ± 1.4 years) were included in this study. Depending on their University of California Los Angeles (UCLA) score at the final follow-up, they were assigned to either the unsatisfactory group (score ≤27; n = 12) or the satisfactory group (score >27; n = 43). Various clinical parameters affecting the clinical outcome were examined through univariate and multivariate analyses. The UCLA score of all patients significantly improved from 18.1 ± 4.4 points preoperatively to 29.8 ± 4.5 points postoperatively ( P muscles, with area under the curve values of 0.79 (sensitivity 91% and specificity 51%) and 0.84 (sensitivity 100% and specificity 54%) in the infraspinatus and subscapularis, respectively. Preoperative fatty degeneration of the infraspinatus and/or subscapularis with Goutallier stage 2 or higher was significantly associated with worse outcome in patients with large/massive tears who had intact tendons after ARCR.

Linear multivariate evaluation models for spatial perception of soundscape.

Science.gov (United States)

Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu

2015-11-01

Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed.

Impact of hospitalisation on the outcome of adolescent anorexia nervosa.

Science.gov (United States)

Gowers, S G; Weetman, J; Shore, A; Hossain, F; Elvins, R

2000-02-01

Owing to the lack of controlled trials of treatment setting in adolescent anorexia nervosa, the benefits and costs of in-patient treatment are not established. To clarify the relationship between a range of presenting features, treatment received and medium- to long-term outcome in adolescent anorexia nervosa. A range of presenting variables were rated for 75 cases of DSM-III-R anorexia nervosa at presentation to an adolescent service, including the Morgan-Russell Global Assessment Score. Cases were followed up at 2-7 years and outcome rated according to reliable methods. Setting of treatment received was also recorded. Two out of 75 cases had died by the time of follow-up. Adequate data for 72 enabled an outcome category to be assigned. The 21 who had received inpatient treatment had a significantly worse outcome than the 51 never admitted to hospital. Multivariate analysis suggests admission to be the major predictor of poor outcome. The benefits and costs of admission to hospital require further investigation, ideally in a randomised-controlled trial. The negative consequences of in-patient treatment are neglected in research.

What Factors are Predictive of Patient-reported Outcomes? A Prospective Study of 337 Shoulder Arthroplasties.

Science.gov (United States)

Matsen, Frederick A; Russ, Stacy M; Vu, Phuong T; Hsu, Jason E; Lucas, Robert M; Comstock, Bryan A

2016-11-01

Although shoulder arthroplasties generally are effective in improving patients' comfort and function, the results are variable for reasons that are not well understood. We posed two questions: (1) What factors are associated with better 2-year outcomes after shoulder arthroplasty? (2) What are the sensitivities, specificities, and positive and negative predictive values of a multivariate predictive model for better outcome? Three hundred thirty-nine patients having a shoulder arthroplasty (hemiarthroplasty, arthroplasty for cuff tear arthropathy, ream and run arthroplasty, total shoulder or reverse total shoulder arthroplasty) between August 24, 2010 and December 31, 2012 consented to participate in this prospective study. Two patients were excluded because they were missing baseline variables. Forty-three patients were missing 2-year data. Univariate and multivariate analyses determined the relationship of baseline patient, shoulder, and surgical characteristics to a "better" outcome, defined as an improvement of at least 30% of the maximal possible improvement in the Simple Shoulder Test. The results were used to develop a predictive model, the accuracy of which was tested using a 10-fold cross-validation. After controlling for potentially relevant confounding variables, the multivariate analysis showed that the factors significantly associated with better outcomes were American Society of Anesthesiologists Class I (odds ratio [OR], 1.94; 95% CI, 1.03-3.65; p = 0.041), shoulder problem not related to work (OR, 5.36; 95% CI, 2.15-13.37; p factors listed above. The area under the receiver operating characteristic curve generated from the cross-validated enhanced predictive model was 0.79 (generally values of 0.7 to 0.8 are considered fair and values of 0.8 to 0.9 are considered good). The false-positive fraction and the true-positive fraction depended on the cutoff probability selected (ie, the selected probability above which the prediction would be classified as

The preoperative manometric pattern predicts the outcome of surgical treatment for esophageal achalasia.

Science.gov (United States)

Salvador, Renato; Costantini, Mario; Zaninotto, Giovanni; Morbin, Tiziana; Rizzetto, Christian; Zanatta, Lisa; Ceolin, Martina; Finotti, Elena; Nicoletti, Loredana; Da Dalt, Gianfranco; Cavallin, Francesco; Ancona, Ermanno

2010-11-01

A new manometric classification of esophageal achalasia has recently been proposed that also suggests a correlation with the final outcome of treatment. The aim of this study was to investigate this hypothesis in a large group of achalasia patients undergoing laparoscopic Heller-Dor myotomy. We evaluated 246 consecutive achalasia patients who underwent surgery as their first treatment from 2001 to 2009. Patients with sigmoid-shaped esophagus were excluded. Symptoms were scored and barium swallow X-ray, endoscopy, and esophageal manometry were performed before and again at 6 months after surgery. Patients were divided into three groups: (I) no distal esophageal pressurization (contraction wave amplitude 30 mmHg); and (III) rapidly propagating pressurization attributable to spastic contractions. Treatment failure was defined as a postoperative symptom score greater than the 10th percentile of the preoperative score (i.e., >7). Type III achalasia coincided with a longer overall lower esophageal sphincter (LES) length, a lower symptom score, and a smaller esophageal diameter. Treatment failure rates differed significantly in the three groups: I = 14.6% (14/96), II = 4.7% (6/127), and III = 30.4% (7/23; p = 0.0007). At univariate analysis, the manometric pattern, a low LES resting pressure, and a high chest pain score were the only factors predicting treatment failure. At multivariate analysis, the manometric pattern and a LES resting pressure achalasia subtypes: patients with panesophageal pressurization have the best outcome after laparoscopic Heller-Dor myotomy.

Extracting bb Higgs Decay Signals using Multivariate Techniques

Energy Technology Data Exchange (ETDEWEB)

Smith, W Clarke; /George Washington U. /SLAC

2012-08-28

For low-mass Higgs boson production at ATLAS at {radical}s = 7 TeV, the hard subprocess gg {yields} h{sup 0} {yields} b{bar b} dominates but is in turn drowned out by background. We seek to exploit the intrinsic few-MeV mass width of the Higgs boson to observe it above the background in b{bar b}-dijet mass plots. The mass resolution of existing mass-reconstruction algorithms is insufficient for this purpose due to jet combinatorics, that is, the algorithms cannot identify every jet that results from b{bar b} Higgs decay. We combine these algorithms using the neural net (NN) and boosted regression tree (BDT) multivariate methods in attempt to improve the mass resolution. Events involving gg {yields} h{sup 0} {yields} b{bar b} are generated using Monte Carlo methods with Pythia and then the Toolkit for Multivariate Analysis (TMVA) is used to train and test NNs and BDTs. For a 120 GeV Standard Model Higgs boson, the m{sub h{sup 0}}-reconstruction width is reduced from 8.6 to 6.5 GeV. Most importantly, however, the methods used here allow for more advanced m{sub h{sup 0}}-reconstructions to be created in the future using multivariate methods.

Multivariate moment closure techniques for stochastic kinetic models

International Nuclear Information System (INIS)

Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

2015-01-01

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs

Multivariate moment closure techniques for stochastic kinetic models

Energy Technology Data Exchange (ETDEWEB)

Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H., E-mail: m.stumpf@imperial.ac.uk [Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ (United Kingdom)

2015-09-07

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

Multivariate Analysis for the Processing of Signals

Directory of Open Access Journals (Sweden)

Beattie J.R.

2014-01-01

Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

Sparse multivariate measures of similarity between intra-modal neuroimaging datasets

Directory of Open Access Journals (Sweden)

Maria J. Rosa

2015-10-01

Full Text Available An increasing number of neuroimaging studies are now based on either combining more than one data modality (inter-modal or combining more than one measurement from the same modality (intra-modal. To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA. However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA, overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labelling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

Multivariate Option Pricing Using Dynamic Copula Models

NARCIS (Netherlands)

van den Goorbergh, R.W.J.; Genest, C.; Werker, B.J.M.

2003-01-01

This paper examines the behavior of multivariate option prices in the presence of association between the underlying assets.Parametric families of copulas offering various alternatives to the normal dependence structure are used to model this association, which is explicitly assumed to vary over

An Efficient Local Algorithm for Distributed Multivariate Regression

Data.gov (United States)

National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...

Outcome of early pars plana vitrectomy in exogenous endophthalmitis

International Nuclear Information System (INIS)

Dar, A.J.; Islam, Q.U.; Hanif, M.K.

2013-01-01

Objective: To evaluate the role of early pars plana vitrectomy (PPV) in cases of exogenous endophthalmitis in terms of final visual outcome and to determine association between various study variables and final visual outcome. Study Design: Quasi experimental study. Place and Duration of Study: Armed Forces Institute of Ophthalmology (AFIO) Rawalpindi, from Aug 2010 to May 2012. Patients and Methods: Eleven cases of exogenous endophthalmitis (post surgical/post traumatic) were managed surgically through 20 G or 23/25 G complete and early PPV. Vitreous aspirate/explanted intraocular lens (IOL) were sent for culture and sensitivity in all cases. Intra and post operative complications were recorded and best corrected visual acuity (BCVA) at 3 months post operative interval was taken as final visual acuity. Results: Median age of study population was 55 years with male preponderance (64%). Approximately 2/3 rd of study population developed endophthalmitis within 6 weeks of surgery/trauma and 55% of patients were operated within 2 weeks of presentation. Positive culture from vitreous aspirate/IOL explant was obtained in 27% of cases. All the patients had initial BCVA in the range of counting finger (CF) at 2 meter to perception of light (PL+). However, 18% of the patients achieved final BCVA of 6/12 or better and 64% achieved final BCVA of 6/36 or better. Maculopathy (macular scar, macular pucker), corneal decompensation, corneal opacity and raised intraocular pressure were the major complications associated with compromised visual outcome. Conclusion: With the advancement in vitreoretinal surgical techniques and availability of more sophisticated viewing and illumination systems, early and complete vitrectomy for post operative or post traumatic endophthalmitis results in favorable visual outcome and early rehabilitation. (author)

Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

Directory of Open Access Journals (Sweden)

Jankulovska Mirjana

2014-01-01

Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

Multivariable calculus with Matlab with applications to geometry and physics

CERN Document Server

Lipsman, Ronald L

2017-01-01

This comprehensive treatment of multivariable calculus focuses on the numerous tools that MATLAB® brings to the subject, as it presents introductions to geometry, mathematical physics, and kinematics. Covering simple calculations with MATLAB®, relevant plots, integration, and optimization, the numerous problem sets encourage practice with newly learned skills that cultivate the reader’s understanding of the material. Significant examples illustrate each topic, and fundamental physical applications such as Kepler’s Law, electromagnetism, fluid flow, and energy estimation are brought to prominent position. Perfect for use as a supplement to any standard multivariable calculus text, a “mathematical methods in physics or engineering” class, for independent study, or even as the class text in an “honors” multivariable calculus course, this textbook will appeal to mathematics, engineering, and physical science students. MATLAB® is tightly integrated into every portion of this book, and its graphical ...

Application of multivariate techniques to analytical data on Aegean ceramics

International Nuclear Information System (INIS)

Bieber, A.M.; Brooks, D.W.; Harbottle, G.; Sayre, E.V.

1976-01-01

The general problems of data collection and handling for multivariate elemental analyses of ancient pottery are considered including such specific questions as the level of analytical precision required, the number and type of elements to be determined and the need for comprehensive multivariate statistical analysis of the collected data in contrast to element by element statistical analysis. The multivariate statistical procedures of clustering in a multidimensional space and determination of the numerical probabilities of specimens belonging to a group through calculation of the Mahalanobis distances for these specimens in multicomponent space are described together with supporting univariate statistical procedures used at Brookhaven. The application of these techniques to the data on Late Bronze Age Aegean pottery (largely previously analysed at Oxford and Brookhaven with some new specimens considered) have resulted in meaningful subdivisions of previously established groups. (author)

Multivariable controller for a 600 MWe CANDU nuclear power plant

International Nuclear Information System (INIS)

Mensah, S.

1982-11-01

The problems of designing a multivariable regulator for a nuclear power station of the Gentilly-2 type are studied. A reduced model, G2LDM, linearized around steady state operating conditions, is derived from the non-linear model G2SIM. The resulting linear model is described by state-space equations. Good agreement is demonstrated between the transient responses of both models. Properties of G2LDM are assessed by performing controllability and observability tests, cyclicity and rank tests, and eigenanalysis. A comprehensive set of application-orinented algorithms which allow multivariable controller design with closed-loop pole-assignment techniques are implemented in a computer-aided design package via several modules. A general scheme for the implementation of a multivariable controller in G2SIM is designed, and simulation tests show satisfactory performance of the controller [fr

Simulation of multivariate diffusion bridges

DEFF Research Database (Denmark)

Bladt, Mogens; Finch, Samuel; Sørensen, Michael

We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously...... proposed simulation method for one-dimensional bridges to the mulit-variate setting. First a method of simulating approzimate, but often very accurate, diffusion bridges is proposed. These approximate bridges are used as proposal for easily implementable MCMC algorithms that produce exact diffusion bridges...

PIXE-quantified AXSIA: Elemental mapping by multivariate spectral analysis

International Nuclear Information System (INIS)

Doyle, B.L.; Provencio, P.P.; Kotula, P.G.; Antolak, A.J.; Ryan, C.G.; Campbell, J.L.; Barrett, K.

2006-01-01

Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other

Thrombus length discrepancy on dual-phase CT can predict clinical outcome in acute ischemic stroke

International Nuclear Information System (INIS)

Park, Mina; Kim, Kyung-eun; Lee, Seung-Koo; Shin, Na-Young; Lim, Soo Mee; Song, Dongbeom; Heo, Ji Hoe; Kim, Jin Woo; Oh, Se Won

2016-01-01

The thrombus length may be overestimated on early arterial computed tomography angiography (CTA) depending on the collateral status. We evaluated the value of a grading system based on the thrombus length discrepancy on dual-phase CT in outcome prediction. Forty-eight acute ischemic stroke patients with M1 occlusion were included. Dual-phase CT protocol encompassed non-contrast enhanced CT, CTA with a bolus tracking technique, and delayed contrast enhanced CT (CECT) performed 40s after contrast injection. The thrombus length discrepancy between CTA and CECT was graded by using a three-point scale: G0 = no difference; G1 = no difference in thrombus length, but in attenuation distal to thrombus; G2 = difference in thrombus length. Univariate and multivariate analyses were performed to define independent predictors of poor clinical outcome at 3 months. The thrombus discrepancy grade showed significant linear relationships with both the collateral status (P = 0.008) and the presence of antegrade flow on DSA (P = 0.010) with good interobserver agreement (κ = 0.868). In a multivariate model, the presence of thrombus length discrepancy (G2) was an independent predictor of poor clinical outcome [odds ratio = 11.474 (1.350-97.547); P =0.025]. The presence of thrombus length discrepancy on dual-phase CT may be a useful predictor of unfavourable clinical outcome in acute M1 occlusion patients. (orig.)

Precision Index in the Multivariate Context

Czech Academy of Sciences Publication Activity Database

Šiman, Miroslav

2014-01-01

Roč. 43, č. 2 (2014), s. 377-387 ISSN 0361-0926 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : data depth * multivariate quantile * process capability index * precision index * regression quantile Subject RIV: BA - General Mathematics Impact factor: 0.274, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/siman-0425059.pdf

Multivariate statistical assessment of coal properties

Czech Academy of Sciences Publication Activity Database

Klika, Z.; Serenčíšová, J.; Kožušníková, Alena; Kolomazník, I.; Študentová, S.; Vontorová, J.

2014-01-01

Roč. 128, č. 128 (2014), s. 119-127 ISSN 0378-3820 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : coal properties * structural,chemical and petrographical properties * multivariate statistics Subject RIV: DH - Mining, incl. Coal Mining Impact factor: 3.352, year: 2014 http://dx.doi.org/10.1016/j.fuproc.2014.06.029

Final Exam Weighting as Part of Course Design

Directory of Open Access Journals (Sweden)

Matthew Franke

2018-03-01

Full Text Available The weighting of a final exam or a final assignment is an essential part of course design that is rarely discussed in pedagogical literature. Depending on the weighting, a final exam or assignment may provide unequal benefits to students depending on their prior performance in the class. Consequently, uncritical grade weighting can discount student learning, by ensuring that improved mastery of material at the semester’s end is not reflected in the course grade. Problems related to several common final exam weights are explored, as are potential solutions to unequal student outcomes made possible by uncritical grade weighting. Ultimately, this essay argues that choosing a weight for a final exam or a final assignment determines what types of student success ought to be possible in the class; therefore, instructors should assign exam weights intentionally, being fully aware of the potential benefits and problems of the weights that they choose.

A Scalable Local Algorithm for Distributed Multivariate Regression

Data.gov (United States)

National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...

The relation of CT-determined tumor parameters and local and regional outcome of tonsillar cancer after definitive radiation treatment

International Nuclear Information System (INIS)

Hermans, Robert; Op de beeck, Katya; Bogaert, Walter van den; Rijnders, Alexis; Staelens, Lorenzo; Feron, Michel; Bellon, Erwin

2001-01-01

Purpose: To investigate the value of CT-derived tumor parameters as predictor of local and regional outcome of tonsillar squamous cell carcinoma treated by definitive radiation therapy. Methods and Materials: The pretreatment CT studies of 112 patients with tonsillar squamous cell carcinoma were reviewed. After redigitizing the films, primary and nodal tumor volume was calculated with the summation-of-areas technique. The nodal CT aspect was graded using a 3-point scale (homogenous, inhomogeneous, and necrotic). Mean follow-up time was 33 months. Actuarial statistical analysis of local and regional outcome was done for each of the covariates; multivariate analysis was performed using Cox's proportional hazards model. Results: In the actuarial analysis, CT-determined primary tumor volume was significantly correlated with local recurrence rate (p<0.05) when all patients were considered, but primary tumor volume did not predict local control within the T2, T3, and T4 category. CT-determined nodal volume was significantly related to regional outcome (p<0.01), but nodal density was not. Total tumor volume was not significantly related to locoregional outcome (p=0.1). In the multivariate analysis, the T and N categories were the independent predictors of local and regional outcomes, respectively. Conclusion: Compared to other head-and-neck sites, primary and nodal tumor volume have only marginal predictive value regarding local and regional outcome after radiation therapy in tonsillar cancer

Associations between HIV-RNA-based indicators and virological and clinical outcomes

DEFF Research Database (Denmark)

Laut, Kamilla G; Shepherd, Leah C; Pedersen, Court

2016-01-01

OBJECTIVES: To evaluate and compare the performance of six HIV-RNA-based quality of care indicators for predicting short-term and long-term outcomes. DESIGN: Multinational cohort study. METHODS: We included EuroSIDA patients on antiretroviral therapy (ART) with at least three viral load measureme......OBJECTIVES: To evaluate and compare the performance of six HIV-RNA-based quality of care indicators for predicting short-term and long-term outcomes. DESIGN: Multinational cohort study. METHODS: We included EuroSIDA patients on antiretroviral therapy (ART) with at least three viral load...... measurements after baseline (the latest of 01/01/2001 or entry into EuroSIDA). Using multivariate Poisson regression, we modelled the association between short-term (resistance, triple-class failure) and long-term (all-cause mortality, any AIDS/non-AIDS clinical event) outcomes and the indicators: viraemia...

Training and evaluation of neural networks for multi-variate time series processing

DEFF Research Database (Denmark)

Fog, Torben L.; Larsen, Jan; Hansen, Lars Kai

1995-01-01

We study the training and generalization for multi-variate time series processing. It is suggested to used a quasi-maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead...... to improved generalization performance. Further, we extend the optimal brain damage pruning technique to the multi-variate case. A key ingredient is an algebraic expression for the generalization ability of a multi-variate model. The variability of the suggested techniques are successfully demonstrated...

Clinical Features and Outcomes Differ between Skeletal and Extraskeletal Osteosarcoma

Directory of Open Access Journals (Sweden)

Sheila Thampi

2014-01-01

Full Text Available Background. Extraskeletal osteosarcoma (ESOS is a rare subtype of osteosarcoma. We investigated patient characteristics, overall survival, and prognostic factors in ESOS. Methods. We identified cases of high-grade osteosarcoma with known tissue of origin in the Surveillance, Epidemiology, and End Results database from 1973 to 2009. Demographics were compared using univariate tests. Overall survival was compared with log-rank tests and multivariate analysis using Cox proportional hazards methods. Results. 256/4,173 (6% patients with high-grade osteosarcoma had ESOS. Patients with ESOS were older, were more likely to have an axial tumor and regional lymph node involvement, and were female. Multivariate analysis showed ESOS to be favorable after controlling for stage, age, tumor site, gender, and year of diagnosis [hazard ratio 0.75 (95% CI 0.62 to 0.90; p=0.002]. There was an interaction between age and tissue of origin such that older patients with ESOS had superior outcomes compared to older patients with skeletal osteosarcoma. Adverse prognostic factors in ESOS included metastatic disease, larger tumor size, older age, and axial tumor site. Conclusion. Patients with ESOS have distinct clinical features but similar prognostic factors compared to skeletal osteosarcoma. Older patients with ESOS have superior outcomes compared to older patients with skeletal osteosarcoma.

Structure formation from non-Gaussian initial conditions: Multivariate biasing, statistics, and comparison with N-body simulations

International Nuclear Information System (INIS)

Giannantonio, Tommaso; Porciani, Cristiano

2010-01-01

We study structure formation in the presence of primordial non-Gaussianity of the local type with parameters f NL and g NL . We show that the distribution of dark-matter halos is naturally described by a multivariate bias scheme where the halo overdensity depends not only on the underlying matter density fluctuation δ but also on the Gaussian part of the primordial gravitational potential φ. This corresponds to a non-local bias scheme in terms of δ only. We derive the coefficients of the bias expansion as a function of the halo mass by applying the peak-background split to common parametrizations for the halo mass function in the non-Gaussian scenario. We then compute the halo power spectrum and halo-matter cross spectrum in the framework of Eulerian perturbation theory up to third order. Comparing our results against N-body simulations, we find that our model accurately describes the numerical data for wave numbers k≤0.1-0.3h Mpc -1 depending on redshift and halo mass. In our multivariate approach, perturbations in the halo counts trace φ on large scales, and this explains why the halo and matter power spectra show different asymptotic trends for k→0. This strongly scale-dependent bias originates from terms at leading order in our expansion. This is different from what happens using the standard univariate local bias where the scale-dependent terms come from badly behaved higher-order corrections. On the other hand, our biasing scheme reduces to the usual local bias on smaller scales, where |φ| is typically much smaller than the density perturbations. We finally discuss the halo bispectrum in the context of multivariate biasing and show that, due to its strong scale and shape dependence, it is a powerful tool for the detection of primordial non-Gaussianity from future galaxy surveys.

Multivariate Analysis of Industrial Scale Fermentation Data

DEFF Research Database (Denmark)

Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

2015-01-01

Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...

Fast and Flexible Multivariate Time Series Subsequence Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

Clinical Infectious Outcomes Associated with Biofilm-related Infections: a Retrospective Chart Review

Science.gov (United States)

2015-06-07

infectious outcomes. Methods: 221 clinical isolates collected from 2005 to 2012 and previously characterized for biofilm formation were studied. Clinical...chronic infection on multivariate analysis. Conclusions: Bacteria species, but not clinical characteristics, were associated with biofilm formation on...the implication of biofilms in a majority of human infections [2]. Biofilm formation also has been linked with poor wound healing [3], burn wound

An Outlyingness Matrix for Multivariate Functional Data Classification

KAUST Repository

Dai, Wenlin

2017-08-25

The classification of multivariate functional data is an important task in scientific research. Unlike point-wise data, functional data are usually classified by their shapes rather than by their scales. We define an outlyingness matrix by extending directional outlyingness, an effective measure of the shape variation of curves that combines the direction of outlyingness with conventional statistical depth. We propose two classifiers based on directional outlyingness and the outlyingness matrix, respectively. Our classifiers provide better performance compared with existing depth-based classifiers when applied on both univariate and multivariate functional data from simulation studies. We also test our methods on two data problems: speech recognition and gesture classification, and obtain results that are consistent with the findings from the simulated data.

CMPH: a multivariate phase-type aggregate loss distribution

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Ren Jiandong

2017-12-01

Full Text Available We introduce a compound multivariate distribution designed for modeling insurance losses arising from different risk sources in insurance companies. The distribution is based on a discrete-time Markov Chain and generalizes the multivariate compound negative binomial distribution, which is widely used for modeling insurance losses.We derive fundamental properties of the distribution and discuss computational aspects facilitating calculations of risk measures of the aggregate loss, as well as allocations of the aggregate loss to individual types of risk sources. Explicit formulas for the joint moment generating function and the joint moments of different loss types are derived, and recursive formulas for calculating the joint distributions given. Several special cases of particular interest are analyzed. An illustrative numerical example is provided.

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Supervisory System and Multivariable Control Applying Weighted Fuzzy-PID Logic in an Alcoholic Fermentation Process

Directory of Open Access Journals (Sweden)

Márcio Mendonça

2015-10-01

Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

Science.gov (United States)

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

TMVA(Toolkit for Multivariate Analysis) new architectures design and implementation.

CERN Document Server

Zapata Mesa, Omar Andres

2016-01-01

Toolkit for Multivariate Analysis(TMVA) is a package in ROOT for machine learning algorithms for classification and regression of the events in the detectors. In TMVA, we are developing new high level algorithms to perform multivariate analysis as cross validation, hyper parameter optimization, variable importance etc... Almost all the algorithms are expensive and designed to process a huge amount of data. It is very important to implement the new technologies on parallel computing to reduce the processing times.

Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors.

Science.gov (United States)

Potyrailo, Radislav A

2017-08-29

For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.

Gender Differences in Intrahousehold Schooling Outcomes: The Role of Sibling Characteristics and Birth-Order Effects

Science.gov (United States)

Rammohan, Anu; Dancer, Diane

2008-01-01

In this paper we examine the influence of gender, sibling characteristics and birth order on the schooling attainment of school-age Egyptian children. We use multivariate analysis to simultaneously examine three different schooling outcomes of a child having "no schooling", "less than the desired level of schooling", and an…

Essentials of multivariate data analysis

CERN Document Server

Spencer, Neil H

2013-01-01

""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

Multivariate Analysis of Schools and Educational Policy.

Science.gov (United States)

Kiesling, Herbert J.

This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

Multivariate ordination statistics workshop with R slides

OpenAIRE

Strack, Michael

2015-01-01

2-hour workshop given at Macquarie University Department of Biological Sciences, 4 November 2015. Workshop was an introduction to the family of techniques falling under multivariate ordination, using the R language and drawing heavily from the book "Numerical Ecology with R" by Borcard et. al (2012).

On Multivariate Methods in Robust Econometrics

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2012-01-01

Roč. 21, č. 1 (2012), s. 69-82 ISSN 1210-0455 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : least weighted squares * heteroscedasticity * multivariate statistics * model selection * diagnostics * computational aspects Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.561, year: 2012 http://www.vse.cz/pep/abstrakt.php?IDcl=411

Current outcome of HLA identical sibling versus unrelated donor transplants in severe aplastic anemia

DEFF Research Database (Denmark)

Bacigalupo, Andrea; Socié, Gerard; Hamladji, Rose Marie

2015-01-01

We have analyzed 1448 patients with acquired aplastic anemia grafted between 2005 and 2009, and compared outcome of identical sibling (n=940) versus unrelated donor (n=508) transplants. When compared to the latter, sibling transplants were less likely to be performed beyond 180 days from diagnosis.......04). In conclusion, in multivariate analysis, the outcome of unrelated donor transplants for acquired aplastic anemia, is currently not statistically inferior when compared to sibling transplants, although patients are at greater risk of acute and chronic graft-versus-host disease. The use of peripheral blood grafts...

Comparison of Spot Sign, Blend Sign and Black Hole Sign for Outcome Prediction in Patients with Intracerebral Hemorrhage.

Science.gov (United States)

Sporns, Peter B; Schwake, Michael; Kemmling, André; Minnerup, Jens; Schwindt, Wolfram; Niederstadt, Thomas; Schmidt, Rene; Hanning, Uta

2017-09-01

Blend sign (BS) and black hole sign (BHS) on non-contrast computed tomography (NCCT) and spot sign (SS) on CT-angiography (CTA) are indicators of early hematoma expansion in spontaneous intracerebral hemorrhage (ICH). However, their independent contributions to outcome have not been well explored. In this retrospective study, inclusion criteria were: 1) spontaneous ICH and 2) NCCT and CTA performed on admission within 6 hours after onset of symptoms. Discharge outcome was dichotomized as good (modified Rankin Scale [mRS] 0-3) and poor (mRS 4-6) outcomes. The impacts of BHS, BS and SS on outcome were assessed in univariate and multivariable logistic regression models. Of 182 patients with spontaneous ICH, 26 (14.3%) presented with BHS, 37 (20.3%) with BS and 39 (21.4%) with SS. There was a substantial correlation between SS and BS (κ=0.701) and a moderate correlation between SS and BHS (κ=0.424). In univariable logistic regression, higher baseline hematoma volume ( P <0.001), intraventricular hemorrhage ( P =0.002) and the presence of BHS/BS/SS (all P <0.001) on admission CT scan were associated with poor outcome. Multivariable analysis identified intraventricular haemorrhage (odds ratio [OR] 2.22 per mL, P =0.022), baseline hematoma volume (OR 1.03 per mL, P <0.001) and SS on CTA (OR 11.43, P <0.001) as independent predictors of poor outcome, showing that SS compared to BS and BHS was more powerful to predict poor outcome. The NCCT BHS and BS are correlated with the CTA SS and are reliable predictors of poor outcome in patients with ICH. Of the CT variables indicating early hematoma expansion, SS on CTA was the most reliable outcome predictor. However, given their correlation with SS on CTA, BS and BHS on NCCT can be useful for predicting outcome if CTA is not obtainable.

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

Science.gov (United States)

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Sparse Linear Identifiable Multivariate Modeling

DEFF Research Database (Denmark)

Henao, Ricardo; Winther, Ole

2011-01-01

and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...

Aspects of multivariate statistical theory

CERN Document Server

Muirhead, Robb J

2009-01-01

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to pen

Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis.

Science.gov (United States)

Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A

2017-06-01

Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at pregression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, pregression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Complications and outcomes of JJ stenting of the ureter in urological practice: A single-centre experience.

Science.gov (United States)

Al-Marhoon, Mohammed S; Shareef, Omar; Venkiteswaran, Krishna P

2012-12-01

To determine the factors affecting the development of complications and the outcomes of JJ stenting. The study included 220 patients (133 males and 87 females, mean age 39.5 years, SD 15.4) who had self-retaining JJ ureteric stents placed while in the authors' institution. Univariate and multivariate analyses were used to identify the significant variables affecting the development of complications and outcome of stenting (condition 'improved' or 'not improved'). Using a modified Clavien classification, there were grade I, II, IIIa, IIIb complications in 67 (30.4%), 39 (17.7%), two (0.9%) and 23 (10.5%) patients, respectively, and none of grades IVa, IVb and V. Loin pain (10.9%) and urinary tract infection (10.9%) were the most common complications, followed by dysuria (7.7%). There were significant complications requiring treatment in 29% of patients, and 71.4% of patients improved after stenting. On multivariate analysis the significant independent factor affecting the complication rate was the stent length (P = 0.016), and the significant independent factor affecting the 'improved' outcome was age (P = 0.014). Longer stents are associated with increased complication rates, and the older the patient the more likely they are to have a poor outcome after stenting. Future prospective multicentre studies with more patients are needed to confirm the present conclusions.

Risk of Local Failure in Breast Cancer Patients With Lobular Carcinoma In Situ at the Final Surgical Margins: Is Re-excision Necessary?

Energy Technology Data Exchange (ETDEWEB)

Sadek, Betro T.; Shenouda, Mina N.; Abi Raad, Rita F. [Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States); Niemierko, Andrzej [Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States); Statistics Section, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States); Keruakous, Amany R. [Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States); Goldberg, Saveli I. [Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States); Statistics Section, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States); Taghian, Alphonse G., E-mail: ataghian@partners.org [Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts (United States)

2013-11-15

Purpose: To compare the outcome of patients with invasive breast cancer both with and without lobular carcinoma in situ (LCIS)-positive/close surgical margins after breast-conserving treatment. Methods and Materials: We retrospectively studied 2358 patients with T1-T2 invasive breast cancer treated with lumpectomy and radiation therapy from January 1980 to December 2009. Median age was 57 years (range, 24-91 years). There were 82 patients (3.5%) with positive/close LCIS margins (<0.2 cm) and 2232 patients (95.7%) with negative margins. A total of 1789 patients (76%) had negative lymph nodes. Patients who received neoadjuvant chemotherapy were excluded. A total of 1783 patients (76%) received adjuvant systemic therapy. Multivariable analysis (MVA) was performed using Cox's proportional hazards model. Results: The 5-year cumulative incidence of locoregional recurrence (LRR) was 3.2% (95% confidence interval [CI] 2.5%-4.1%) for the 2232 patients with LCIS-negative surgical margins (median follow-up 104 months) and 2.8% (95% CI 0.7%-10.8%) for the 82 patients with LCIS-positive/close surgical margins (median follow-up 90 months). This was not statistically significant (P=.5). On MVA, LCIS-positive margins after the final surgery were not associated with increased risk of LRR (hazard ratio [HR] 3.4, 95% CI 0.5-24.5, P=.2). Statistically significant prognostic variables on Cox's MVA for risk of LRR included systemic therapy (HR 0.5, 95% CI 0.33-0.75, P=.001), number of positive lymph nodes (HR 1.11, 95% CI 1.05-1.18, P=.001), menopausal status (HR 0.96, 95% CI 0.95-0.98, P=.001), and histopathologic grade (grade 3 vs grade 1/2) (HR 2.6, 95% CI 1.4-4.7, P=.003). Conclusion: Our results suggest that the presence of LCIS at the surgical margin after lumpectomy does not increase the risk of LRR or the final outcome. These findings suggest that re-excision or mastectomy in patients with LCIS-positive/close final surgical margins is unnecessary.

Simulation research on multivariable fuzzy model predictive control of nuclear power plant

International Nuclear Information System (INIS)

Su Jie

2012-01-01

To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)

Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

Directory of Open Access Journals (Sweden)

Lijun Wang

2013-01-01

Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Design of multivariable controller for a 600 MWe CANDU nuclear power plant

International Nuclear Information System (INIS)

Mensah, S.; McMorran, P.D.

1982-04-01

This paper reports the results of a case study on the design of a multivariable regulator for a nuclear power station of the Gentilly-2 type. In this study, a design model was derived by simplifying and linearizing equations in the G2SIM non-linear model. Open-loop simulation showed good agreement between transient responses of both models. After a critical review of multivariable design techniques, the authors explored pole shifting with output feedback. A comprehensive set of application-oriented algorithms for closed-loop pole shifting, implemented via modules in the MVPACK computer-aided design package were derived. A controller was designed for the linear model, then implemented on the non-linear simulation. After adjustment of controller gains, mainly in the dynanamic section of the feedback, simulation results showed that the performance of the multivariable controller on G2SIM is satisfactory. The results demonstrate the relative superiority of the multi-variable controller over the existing conventional controller

Conversion from laparoscopic to open cholecystectomy: Multivariate analysis of preoperative risk factors

Directory of Open Access Journals (Sweden)

Khan M

2005-01-01

Full Text Available BACKGROUND: Laparoscopic cholecystectomy has become the gold standard in the treatment of symptomatic cholelithiasis. Some patients require conversion to open surgery and several preoperative variables have been identified as risk factors that are helpful in predicting the probability of conversion. However, there is a need to devise a risk-scoring system based on the identified risk factors to (a predict the risk of conversion preoperatively for selected patients, (b prepare the patient psychologically, (c arrange operating schedules accordingly, and (d minimize the procedure-related cost and help overcome financial constraints, which is a significant problem in developing countries. AIM: This study was aimed to evaluate preoperative risk factors for conversion from laparoscopic to open cholecystectomy in our setting. SETTINGS AND DESIGNS: A case control study of patients who underwent laparoscopic surgery from January 1997 to December 2001 was conducted at the Aga Khan University Hospital, Karachi, Pakistan. MATERIALS AND METHODS: All those patients who were converted to open surgery (n = 73 were enrolled as cases. Two controls who had successful laparoscopic surgery (n = 146 were matched with each case for operating surgeon and closest date of surgery. STATISTICAL ANALYSIS USED: Descriptive statistics were computed and, univariate and multivariate analysis was done through multiple logistic regression. RESULTS: The final multivariate model identified two risk factors for conversion: ultrasonographic signs of inflammation (adjusted odds ratio [aOR] = 8.5; 95% confidence interval [CI]: 3.3, 21.9 and age > 60 years (aOR = 8.1; 95% CI: 2.9, 22.2 after adjusting for physical signs, alkaline phosphatase and BMI levels. CONCLUSION: Preoperative risk factors evaluated by the present study confirm the likelihood of conversion. Recognition of these factors is important for understanding the characteristics of patients at a higher risk of conversion.

Various forms of indexing HDMR for modelling multivariate classification problems

Energy Technology Data Exchange (ETDEWEB)

Aksu, Çağrı [Bahçeşehir University, Information Technologies Master Program, Beşiktaş, 34349 İstanbul (Turkey); Tunga, M. Alper [Bahçeşehir University, Software Engineering Department, Beşiktaş, 34349 İstanbul (Turkey)

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.

Multivariate analysis of factors influencing the effect of radiosynovectomy

International Nuclear Information System (INIS)

Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr.; Wendler, J.; Kenn, W.; Reidemeister, C.

2002-01-01

Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [de

Non-fragile multivariable PID controller design via system augmentation

Science.gov (United States)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS

OpenAIRE

Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita

2016-01-01

This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1

On the Optimality of Multivariate S-Estimators

NARCIS (Netherlands)

Croux, C.; Dehon, C.; Yadine, A.

2010-01-01

In this paper we maximize the efficiency of a multivariate S-estimator under a constraint on the breakdown point. In the linear regression model, it is known that the highest possible efficiency of a maximum breakdown S-estimator is bounded above by 33% for Gaussian errors. We prove the surprising

Ranking multivariate GARCH models by problem dimension

NARCIS (Netherlands)

M. Caporin (Massimiliano); M.J. McAleer (Michael)

2010-01-01

textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to

Multivariate techniques of analysis for ToF-E recoil spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1996-12-31

Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

Multivariate techniques of analysis for ToF-E recoil spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Whitlow, H J; Bouanani, M E; Persson, L; Hult, M; Jonsson, P; Johnston, P N [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M; Zaring, C [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P N; Bubb, I F; Walker, B R; Stannard, W B [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D D; Dytlewski, N [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1997-12-31

Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

Directional preference and functional outcomes among subjects classified at high psychosocial risk using STarT.

Science.gov (United States)

Werneke, Mark W; Edmond, Susan; Young, Michelle; Grigsby, David; McClenahan, Brian; McGill, Troy

2018-03-14

Physiotherapy has an important role in managing patients with non-specific low back pain who experience elevated psychosocial distress or risk for chronic disability. In terms of evidence-based physiotherapy practice, cognitive-behavioural approaches for patients at high psychosocial risk are the recommended management to improve patient treatment outcomes. Evidence also suggests that directional preference (DP) is an important treatment effect modifier for prescribing specific exercises for patients to improve outcomes. Little is known about the influence of treatment techniques based on DP on outcomes for patients classified as high psychosocial risk using the Subgroups for Targeted Treatment (STarT) Back Screening Tool. This study aimed to examine the association between functional status (FS) at rehabilitation discharge for patients experiencing low back pain classified at high STarT psychosocial risk and whose symptoms showed a DP versus No-DP. High STarT risk patients (n = 138) completed intake surveys, that is, the lumbar FS of Focus On Therapeutic Outcomes, Inc., and STarT, and were evaluated for DP by physiotherapists credentialed in McKenzie methods. The FS measure of Focus On Therapeutic Outcomes, Inc., was repeated at discharge. DP and No-DP prevalence rates were calculated. Associations between first-visit DP and No-DP and change in FS were assessed using univariate and multivariate regression models controlling for 11 risk-adjusted variables. One hundred nine patients classified as high STarT risk had complete intake and discharge FS and DP data. Prevalence rate for DP was 65.1%. A significant and clinically important difference (7.98 FS points; p = .03) in change in function at discharge between DP and No-DP was observed after controlling for all confounding variables in the final model. Findings suggest that interventions matched to DP are effective for managing high psychological risk patients and may provide physiotherapists with an

Neoadjuvant versus definitive chemoradiotherapy for locally advanced esophageal cancer. Outcomes and patterns of failure

Energy Technology Data Exchange (ETDEWEB)

Haefner, Matthias Felix; Lang, Kristin; Koerber, Stefan Alexander; Debus, Juergen [University Hospital of Heidelberg, Department of Radiation Oncology, Heidelberg (Germany); National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg (Germany); Verma, Vivek [University of Nebraska Medical Center, Department of Radiation Oncology, Omaha, NE (United States); Uhlmann, Lorenz [University of Heidelberg, Institute of Medical Biometry and Informatics (IMBI), Heidelberg (Germany); Sterzing, Florian [National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg (Germany); Hospital Kempten, Department of Radiation Oncology, Kempten (Germany)

2018-02-15

Randomized trials examining neoadjuvant chemoradiotherapy followed by surgical resection (nCRT-S) and definitive CRT (dCRT) for esophageal cancer (EC) patients are hampered by use of nonstandard treatment paradigms. Outcomes of nCRT-S versus dCRT in a more common patient population are lacking. We investigated local control and survival, evaluated clinical factors associated with endpoints, and assessed patterns of failure between these cohorts. We retrospectively analyzed 130 patients with locally advanced EC receiving either dCRT or nCRT-S at our institution from 2000-2012. Inclusion criteria were curatively treated nonmetastatic EC, Karnofsky performance status ≥70%, and receipt of concomitant CRT. Patients were excluded if receiving <41 Gy neoadjuvantly or <50 Gy definitively. Kaplan-Meier analysis was used to evaluate local recurrence (LR), progression-free survival (PFS), and overall survival (OS). Univariate and multivariate Cox proportional hazards modeling addressed factors associated with outcomes. Patterns of failure were enumerated as local, regional, or distant. Mean follow-up was 34.2 months. The 3-year LR was 10.8% in the nCRT-S group and 21.5% in the dCRT group (p = 0.266). Median PFS were 15.6 and 14.9 months, respectively (p = 0.549). Median OS were 20.6 and 25.9 months, respectively (p = 0.81). On univariate and multivariate analysis, none of the investigated factors was associated with outcomes, although node-positive disease showed a trend for worse OS and PFS. Most common failures in both groups were distant (dCRT 31.2% vs. nCRT-S 21.6%) followed by local in-field recurrences (dCRT 26.9% vs. nCRT-S 10.8%). In this institutional analysis, no significant differences regarding outcomes and patterns of failure were observed between nCRT-S and dCRT. (orig.) [German] Randomisierte Studien, welche die neoadjuvante Radiochemotherapie (CRT) einschliesslich konsekutiver Operation (nCRT-S) mit der definitiven Radiochemotherapie (dCRT) fuer

Multivariate Welch t-test on distances

OpenAIRE

Alekseyenko, Alexander V.

2016-01-01

Motivation: Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method...

Modeling Covariance Breakdowns in Multivariate GARCH

OpenAIRE

Jin, Xin; Maheu, John M

2014-01-01

This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...

Disturbance Error Reduction in Multivariable Optimal Control Systems

Directory of Open Access Journals (Sweden)

Ole A. Solheim

1983-01-01

Full Text Available The paper deals with the design of optimal multivariable controllers, using a modified LQR approach. All controllers discussed contain proportional feedback and, in addition, there may be feedforward, integral action or state estimation.

Multivariable H force/level control of the twin-roller strip caster

International Nuclear Information System (INIS)

Cavazos, A.; Edwards, J.B.

2005-01-01

Twin-roller steel strip casters may offer some advantages with respect to classical continuous casting hot rolling processes. Some works have reported control aspects of this process and although the process has been found to be highly interactive and non-linear, little or no attention has been given to its multivariable characteristics. The purpose of this work is to design a multivariable control capable of decoupling the system. This paper presents some important aspects of the strip caster modeling and reports the simulation results of the application of the multivariable H-optimal control for nominal performance to force/level control. Various controllers have been designed for different pool level heights and it is shown that they can decouple the system, allowing the application of PI decentralized controllers to considerably improve performance. (author)

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

Science.gov (United States)

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

A multiresolution approach for the convergence acceleration of multivariate curve resolution methods.

Science.gov (United States)

Sawall, Mathias; Kubis, Christoph; Börner, Armin; Selent, Detlef; Neymeyr, Klaus

2015-09-03

Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements. The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models. Copyright © 2015 Elsevier B.V. All rights reserved.

Effectiveness of Multivariate Time Series Classification Using Shapelets

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

Multivariate spectral-analysis of movement-related EEG data

International Nuclear Information System (INIS)

Andrew, C. M.

1997-01-01

The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

Sunspot Cycle Prediction Using Multivariate Regression and Binary ...

Indian Academy of Sciences (India)

49

Multivariate regression model has been derived based on the available cycles 1 .... The flare index correlates well with various parameters of the solar activity. ...... 32) Sabarinath A and Anilkumar A K 2011 A stochastic prediction model for the.

Multivariate statistical characterization of groundwater quality in Ain ...

African Journals Online (AJOL)

Administrator

depends much on the sustainability of the available water resources. Water of .... 18 wells currently in use were selected based on the preliminary field survey carried out to ... In recent times, multivariate statistical methods have been applied ...

Prehospital Intubation and Outcome in Traumatic Brain Injury—Assessing Intervention Efficacy in a Modern Trauma Cohort

Directory of Open Access Journals (Sweden)

Rebecka Rubenson Wahlin

2018-04-01

Full Text Available BackgroundPrehospital intubation in traumatic brain injury (TBI focuses on limiting the effects of secondary insults such as hypoxia, but no indisputable evidence has been presented that it is beneficial for outcome. The aim of this study was to explore the characteristics of patients who undergo prehospital intubation and, in turn, if these parameters affect outcome.Material and methodsPatients ≥15 years admitted to the Department of Neurosurgery, Stockholm, Sweden with TBI from 2008 through 2014 were included. Data were extracted from prehospital and hospital charts, including prospectively collected Glasgow Outcome Score (GOS after 12 months. Univariate and multivariable logistic regression models were employed to examine parameters independently correlated to prehospital intubation and outcome.ResultsA total of 458 patients were included (n = 178 unconscious, among them, n = 61 intubated. Multivariable analyses indicated that high energy trauma, prehospital hypotension, pupil unresponsiveness, mode of transportation, and distance to the hospital were independently correlated with intubation, and among them, only pupil responsiveness was independently associated with outcome. Prehospital intubation did not add independent information in a step-up model versus GOS (p = 0.154. Prehospital reports revealed that hypoxia was not the primary cause of prehospital intubation, and that the procedure did not improve oxygen saturation during transport, while an increasing distance from the hospital increased the intubation frequency.ConclusionIn this modern trauma cohort, prehospital intubation was not independently associated with outcome; however, hypoxia was not a common reason for prehospital intubation. Prospective trials to assess efficacy of prehospital airway intubation will be difficult due to logistical and ethical considerations.

Low free triiodothyronine levels are related to symptomatic intracranial hemorrhage and poor functional outcomes after intravenous thrombolysis in acute ischemic stroke patients.

Science.gov (United States)

Liu, Junfeng; Wang, Deren; Xiong, Yao; Yuan, Ruozhen; Tao, Wendan; Liu, Ming

2016-05-01

Low free triiodothyronine (fT3) levels have been associated with increased mortality and poor functional outcomes in patients with stroke. However, the research of relationship between fT3 levels and acute ischemic stroke (AIS) patients with intravenous thrombolysis (IVT) is scarce. We aimed to investigate the association of fT3 levels with symptomatic intracranial hemorrhage (sICH) and functional outcomes at discharge in AIS patients with IVT. Patients with AIS admitted to West China hospital, Sichuan University, who had underwent IVT treatment, were consecutively and retrospectively included. Demographic and clinical information were collected and analyzed according to the levels of fT3. We used logistic regression analysis to estimate the multivariable adjusted association of fT3 levels and post-IVT sICH, and functional outcomes at discharge. Among the 46 patients (26 males; mean age, 63.6 years) in the final analysis, 17 patients (37.0%) had fT3 levels lower than the reference range. After adjustment for age, gender, and statistically important variables (NIHSS on admission, urea levels and creatinine levels), low fT3 levels were significantly associated with post-IVT sICH (p = 0.01, OR = 0.27, 95% CI 0.10-0.77) and poor functional outcomes at discharge (p = 0.04 OR = 2.58, 95% CI 1.05-6.35). We found that lower free T3 levels are independently related to post-IVT sICH and poor functional outcomes at discharge in AIS patients with IVT, which should be verified and extended in large cohorts in the future.

MULTIVARIATE ACCOUNTING IN INTERNATIONAL FINANCIAL REPORTING STANDARDS

Directory of Open Access Journals (Sweden)

V. V. IEVDOKYMOV

2017-03-01

Full Text Available The necessity of the research on the basis of the positivist model of scientific knowledge is proved. The value of the conceptual framework in the process of bookkeeping selection is analyzed. The differences of the accounting selection adjustment procedure in US GAAP and IFRS are considered. The role and importance of the qualitative characteristics of financial reporting in the implementation of accounting selection are substantiated. The structure of the qualitative characteristics of financial reporting and their limitations under the Conceptual Framework for the preparation and presentation of financial statements are examined. The correlation between the accounting rules and alternatives adopted in US GAAP and IAS / IFRS is analyzed. The necessity to discuss the issue of the feasibility of «rule-oriented» or «principle-oriented» accounting model in the context of multivariate concept is studied. The authors prove the necessity of the application of institutional theory to solve the problems of accounting opportunism that arises when using the concept of multivariate accounting in International Financial Reporting Standards.

Particulate characterization by PIXE multivariate spectral analysis

International Nuclear Information System (INIS)

Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.

2007-01-01

Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media

A multivariate time series approach to modeling and forecasting demand in the emergency department.

Science.gov (United States)

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Likelihood estimators for multivariate extremes

KAUST Repository

Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.

2015-01-01

The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.

Likelihood estimators for multivariate extremes

KAUST Repository

Huser, Raphaël

2015-11-17

The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.

Outcome and CT differentiation of gallbladder neuroendocrine tumours from adenocarcinomas

Energy Technology Data Exchange (ETDEWEB)

Kim, Tae-Hyung [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of); Kim, Se Hyung [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of); Seoul National University Hospital and Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Lee, Kyoung Boon [Seoul National University Hospital, Department of Pathology, Seoul (Korea, Republic of); Han, Joon Koo [Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Seoul National University Medical Research Center, Institute of Radiation Medicine, Seoul (Korea, Republic of)

2017-02-15

To retrospectively investigate clinical outcome and differential CT features of gallbladder (GB) neuroendocrine tumours (NETs) from adenocarcinomas (ADCs). Nineteen patients with poorly-differentiated (PD) NETs and 19 patients with PD ADCs were enrolled. Clinical outcome was compared by the Kaplan-Meier method. We assessed qualitative and quantitative CT features to identify significant differential CT features of PD NETs from ADCs using univariate and multivariate analyses. Receiver operating characteristic (ROC) analysis was used for quantitative CT features. PD NETs showed poorer prognosis with significantly shorter median survival days than ADCs (363 vs. 590 days, P = 0.03). On univariate analysis, NETs more frequently manifested as GB-replacing type and showed well-defined margins accompanied with intact overlying mucosa. On multivariate analysis, well-defined margin was the sole significant CT differentiator (odds ratio = 27.817, P = 0.045). Maximum size of hepatic and lymph node (LN) metastases was significantly larger in NETs (11.0 cm and 4.62 cm) than ADCs (2.40 cm and 2.41 cm). Areas under ROC curves for tumour-to-mucosa ratio, maximum size of hepatic and LN metastasis were 0.772, 0.932 and 0.919, respectively (P < 0.05). GB PD NETs show poorer prognosis than ADCs. Well-defined margin, larger hepatic and LN metastases are useful CT differentiators of GB NETs from ADCs. (orig.)

Multivariant design and multiple criteria analysis of building refurbishments

Energy Technology Data Exchange (ETDEWEB)

Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. [Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius (Lithuania)

2005-07-01

In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great many of factors, including: cost of refurbishment, annual fuel economy after refurbishment, tentative pay-back time, harmfulness to health of the materials used, aesthetics, maintenance properties, functionality, comfort, sound insulation and longevity, etc. Solutions of an alternative character allow for a more rational and realistic assessment of economic, ecological, legislative, climatic, social and political conditions, traditions and for better the satisfaction of customer requirements. They also enable one to cut down on refurbishment costs. In carrying out the multivariant design and multiple criteria analysis of a building refurbishment much data was processed and evaluated. Feasible alternatives could be as many as 100,000. How to perform a multivariant design and multiple criteria analysis of alternate alternatives based on the enormous amount of information became the problem. Method of multivariant design and multiple criteria of a building refurbishment's analysis were developed by the authors to solve the above problems. In order to demonstrate the developed method, a practical example is presented in this paper. (author)

Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

Science.gov (United States)

Hwang, Chih-Lyang; Jan, Chau

2016-02-01

At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

Multivariate network exploration and presentation : from detail to overview via selections and aggregations

NARCIS (Netherlands)

Elzen, van den S.J.; Wijk, van J.J.

2014-01-01

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently,

A unifying framework for k-statistics, polykays and their multivariate generalizations.

OpenAIRE

DI NARDO, Elvira; GUARINO G, G.; Senato, D.

2008-01-01

Through the classical umbral calculus, we provide a unifying syntax for single and multivariate $k$-statistics, polykays and multivariate polykays. From a combinatorial point of view, we revisit the theory as exposed by Stuart and Ord, taking into account the Doubilet approach to symmetric functions. Moreover, by using exponential polynomials rather than set partitions, we provide a new formula for $k$-statistics that results in a very fast algorithm to generate such estimators.

Determinants of long-term outcome in patients undergoing simultaneous resection of synchronous colorectal liver metastases.

Directory of Open Access Journals (Sweden)

Qi Lin

Full Text Available BACKGROUND: It remains unclear which patients can benefit from simultaneous resection of synchronous colorectal liver metastases (SCRLMs. This study aimed to examine the prognostic value of patient- and tumor-related factors in predicting long-term outcomes of patients undergoing simultaneous resection of SCRLMs and to help patients select a suitable therapeutic regimen and proper surveillance. METHODS: Clinicopathological and outcome data of 154 consecutive SCRLM patients who underwent simultaneous resection between July 2003 and July 2013 were collected from our prospectively established SCRLM data and analyzed with univariate and multivariate methods, and the prognostic index (PI was formulated based on the regression coefficients (β of the Cox model. The patients were classified into high- and low-risk groups according to the PI value; the cut-off point was the third quartile. RESULTS: The 5-year overall survival rate was 46%, and the 5-year disease-free survival rate was 35%. Five factors were found to be independent predictors of poor overall survival (OS by multivariate analysis: positive lymph node status, vascular invasion, BRAF mutation, the distribution of bilobar liver metastases (LMs and non-R0 resection of LMs. Compared to low PI (≤5.978, high PI (>5.978 was highly predictive of shorter OS. Three factors were found to be independent predictors of poor disease-free survival (DFS by multivariate analysis: tumor deposits, BRAF mutation and bilobar LM distribution. We also determined the PI for DFS. Compared to low PI (≤2.945, high PI (>2.945 was highly predictive of shorter DFS. CONCLUSIONS: Simultaneous resection of SCRLM may lead to various long-term outcomes. Patients with low PI have longer OS and DFS, while those with high PI have shorter OS and DFS. Thus, patients with high PI may receive more aggressive treatment and intensive surveillance, This model needs further validation.

A short note on multivariate dependence modeling

Czech Academy of Sciences Publication Activity Database

Bína, V.; Jiroušek, Radim

2013-01-01

Roč. 49, č. 3 (2013), s. 420-432 ISSN 0023-5954 Grant - others:GA ČR(CZ) GAP403/12/2175 Program:GA Institutional support: RVO:67985556 Keywords : multivariate distribution * dependence * copula Subject RIV: IN - Informatics, Computer Science Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2014/MTR/jirousek-0427848.pdf

Multivariate analysis of quality of life outcome for nasopharyngeal carcinoma patients after treatment

International Nuclear Information System (INIS)

Fang, Fu-Min; Tsai, Wen-Ling; Lee, Tsair-Fwu; Liao, Kuan-Cho; Chen, Hui-Chun; Hsu, Hsuan-Chih

2010-01-01

Purpose: The study analyzed the prognostic factors of quality of life (QoL) for patients with nasopharyngeal carcinoma (NPC) after treatment, with focusing on the therapeutic benefits of the technological advances in radiotherapy (RT). Materials and methods: A cross-sectional investigation was conducted to assess the QoL of 356 NPC patients with cancer-free survival of more than 2 years. Among them, 106 patients were treated by two-dimensional RT (2DRT), 108 by 2DRT plus three-dimensional conformal RT (3DCRT) boost, 58 by 3DCRT alone, and 84 by intensity-modulated RT (IMRT). The QoL was assessed by the EORTC QLQ-C30 questionnaire and QLQ-H and N35 module. The clinical difference of QoL scores between groups was calculated using Cohen's D coefficient. Results: We found NPC survivors who had a higher education level or annual family income and who had received more advanced RT treatments had better QoL outcomes. Compared with 2DRT, the impact of 3DCRT was small on most scales and moderate (Cohen's D: 0.53-0.67) on emotional functioning, pain, and mouth opening; the impact of IMRT was moderate on nine scales and large (Cohen's D: 0.80-0.88) on swallowing, social eating, teeth, and mouth opening. Conclusions: In addition to socioeconomic levels, advances in RT technique played a significant role in improving QoL of NPC patients.

Robot-assisted partial nephrectomy for hilar tumors: perioperative outcomes.

Science.gov (United States)

Eyraud, Rémi; Long, Jean-Alexandre; Snow-Lisy, Devon; Autorino, Riccardo; Hillyer, Shahab; Klink, Joseph; Rizkala, Emad; Stein, Robert J; Kaouk, Jihad H; Haber, Georges-Pascal

2013-06-01

To compare perioperative outcomes of robot-assisted partial nephrectomy (RAPN) for hilar vs nonhilar tumors. The study retrospectively reviewed 364 patients with available computed tomography scans undergoing RAPN. Demographic data and perioperative outcomes results were compared between the hilar (group 1, n = 70) and nonhilar tumors (group 2, n = 294). Multivariate analysis was used to identify predictors of warm ischemia time (WIT), estimated blood loss (EBL), major perioperative complications, and postoperative renal function. There were no differences with respect to demographic variables. Hilar tumors had higher RENAL (radius, exophytic/endophytic properties of the tumor, nearness of tumor deepest portion to the collecting system or sinus, anterior/posterior descriptor and the location relative to polar lines) scores (P hilar tumors were associated with greater operative time (210 vs 180 minutes, P hilar vs nonhilar patients on postoperative day 3 (70.12 vs 74.71 mL/min/1.73 m(2), P = .31) or at last follow-up (72.62 vs 75.78 mL/min/1.73 m(2), P = .40), respectively. Multivariate analysis found hilar location was independently associated with increased WIT without significant changes in EBL, major complications, or postoperative renal function. RAPN represents a safe and effective procedure for hilar tumors. Hilar location for patients undergoing RAPN in a high-volume institution seems not be associated with an increased risk of transfusions, major complications, or decline of early postoperative renal function. Copyright © 2013 Elsevier Inc. All rights reserved.

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

Science.gov (United States)

Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

Multivariate Variance Targeting in the BEKK-GARCH Model

DEFF Research Database (Denmark)

Pedersen, Rasmus Søndergaard; Rahbek, Anders

2014-01-01

This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By definition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modified likelihood function, or estimating function, corresponding...

Multivariable Frequency Response Functions Estimation for Industrial Robots

NARCIS (Netherlands)

Hardeman, T.; Aarts, Ronald G.K.M.; Jonker, Jan B.

2005-01-01

The accuracy of industrial robots limits its applicability for high demanding processes, like robotised laser welding. We are working on a nonlinear exible model of the robot manipulator to predict these inaccuracies. This poster presents the experimental results on estimating the Multivariable

Is ovarian hyperstimulation associated with higher blood pressure in 4-year-old IVF offspring? Part I: multivariable regression analysis.

Science.gov (United States)

Seggers, Jorien; Haadsma, Maaike L; La Bastide-Van Gemert, Sacha; Heineman, Maas Jan; Middelburg, Karin J; Roseboom, Tessa J; Schendelaar, Pamela; Van den Heuvel, Edwin R; Hadders-Algra, Mijna

2014-03-01

COH-IVF group than in the Sub-NC group (B: 0.28; 95% CI: 0.03-0.53). Larger study groups are necessary to draw firm conclusions. An effect of gender or ICSI could not be properly investigated as stratifying would further reduce the sample size. We corrected for the known differences between MNC-IVF and COH-IVF but it is possible that the groups differ in additional, more subtle parental characteristics. In addition, we measured BP on 1 day only, had no control group of children born to fertile couples (precluding investigating effects of the underlying subfertility) and included singletons only. As COH-IVF is associated with multiple births we may have underestimated cardiometabolic problems after COH-IVF. Finally, multivariable regression analysis does not provide clear insight in the causal mechanisms and we have performed further explorative analyses. Our findings are in line with other studies describing adverse effects of IVF on cardiometabolic outcome but this is the first study suggesting that ovarian hyperstimulation, as used in IVF treatments, could be a causative mechanism. Perhaps ovarian hyperstimulation negatively influences cardiometabolic outcome via changes in the early environment of the oocyte and/or embryo that result in epigenetic modifications of key metabolic systems that are involved in BP regulation. Future research needs to assess further the role of ovarian hyperstimulation in poorer cardiometabolic outcome and investigate the underlying mechanisms. The findings emphasize the importance of cardiometabolic monitoring of the growing number of children born following IVF. The authors have no conflicts of interest to declare. The study was supported by the University Medical Center Groningen, the Cornelia Foundation and the school for Behavioral- and Cognitive Neurosciences. The sponsors of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report.

Measures of dependence for multivariate Lévy distributions

Science.gov (United States)

Boland, J.; Hurd, T. R.; Pivato, M.; Seco, L.

2001-02-01

Recent statistical analysis of a number of financial databases is summarized. Increasing agreement is found that logarithmic equity returns show a certain type of asymptotic behavior of the largest events, namely that the probability density functions have power law tails with an exponent α≈3.0. This behavior does not vary much over different stock exchanges or over time, despite large variations in trading environments. The present paper proposes a class of multivariate distributions which generalizes the observed qualities of univariate time series. A new consequence of the proposed class is the "spectral measure" which completely characterizes the multivariate dependences of the extreme tails of the distribution. This measure on the unit sphere in M-dimensions, in principle completely general, can be determined empirically by looking at extreme events. If it can be observed and determined, it will prove to be of importance for scenario generation in portfolio risk management.

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

Science.gov (United States)

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Physical status of human papillomavirus integration in cervical cancer is associated with treatment outcome of the patients treated with radiotherapy.

Directory of Open Access Journals (Sweden)

Hye-Jin Shin

Full Text Available Integration of human papillomavirus (HPV DNA into the host genome is a critical aetiological event in the progression from normal cervix to intraepithelial neoplasm, and finally to invasive cervical cancer. However, there has been little work on how HPV integration status relates to treatment outcome for cervical carcinomas. In the current study, HPV E2 and E6 gene copy numbers were measured in 111 cervical cancer tissues using real-time QPCR. Integration patterns were divided into four groups: single copy-integrated with episomal components (group 1, single copy-integrated without episomal components (group 2, multicopy tandem repetition-integrated (group 3, and low HPV (group 4 groups. A relapse-predicting model was constructed using multivariable Cox proportional hazards model to classify patients into different risk groups for disease-free survival (DFS. The model was internally validated using bootstrap resampling. Oligonucleotide microarray analysis was performed to evaluate gene expression patterns in relation to the different integration groups. DFS rate was inferior in the order of the patients in group 4, group 2/3, and group 1. Multivariate analysis showed that histologic grade, clinical stage group, and integration pattern were significant prognostic factors for poor DFS. The current prognostic model accurately predicted the risk of relapse, with an area under the receiver operating characteristic curve (AUC of 0.74 (bootstrap corrected, 0.71. In conclusion, these data suggest that HPV integration pattern is a potent prognostic factor for tailored treatment of cervical cancer.

Multivariate diagnostics and anomaly detection for nuclear safeguards

International Nuclear Information System (INIS)

Burr, T.

1994-01-01

For process control and other reasons, new and future nuclear reprocessing plants are expected to be increasingly more automated than older plants. As a consequence of this automation, the quantity of data potentially available for safeguards may be much greater in future reprocessing plants than in current plants. The authors first review recent literature that applies multivariate Shewhart and multivariate cumulative sum (Cusum) tests to detect anomalous data. These tests are used to evaluate residuals obtained from a simulated three-tank problem in which five variables (volume, density, and concentrations of uranium, plutonium, and nitric acid) in each tank are modeled and measured. They then present results from several simulations involving transfers between the tanks and between the tanks and the environment. Residuals from a no-fault problem in which the measurements and model predictions are both correct are used to develop Cusum test parameters which are then used to test for faults for several simulated anomalous situations, such as an unknown leak or diversion of material from one of the tanks. The leak can be detected by comparing measurements, which estimate the true state of the tank system, with the model predictions, which estimate the state of the tank system as it ''should'' be. The no-fault simulation compares false alarm behavior for the various tests, whereas the anomalous problems allow one to compare the power of the various tests to detect faults under possible diversion scenarios. For comparison with the multivariate tests, univariate tests are also applied to the residuals

A simplified parsimonious higher order multivariate Markov chain model

Science.gov (United States)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

An investigation of client mood in the initial and final sessions of cognitive-behavioral therapy and psychodynamic-interpersonal therapy.

Science.gov (United States)

Mcclintock, Andrew S; Stiles, William B; Himawan, Lina; Anderson, Timothy; Barkham, Michael; Hardy, Gillian E

2016-01-01

Our aim was to examine client mood in the initial and final sessions of cognitive-behavioral therapy (CBT) and psychodynamic-interpersonal therapy (PIT) and to determine how client mood is related to therapy outcomes. Hierarchical linear modeling was applied to data from a clinical trial comparing CBT with PIT. In this trial, client mood was assessed before and after sessions with the Session Evaluation Questionnaire-Positivity Subscale (SEQ-P). In the initial sessions, CBT clients had higher pre-session and post-session SEQ-P ratings and greater pre-to-post session mood change than did clients in PIT. In the final sessions, these pre, post, and change scores were generally equivalent across CBT and PIT. CBT outcome was predicted by pre- and post-session SEQ-P ratings from both the initial sessions and the final sessions of CBT. However, PIT outcome was predicted by pre- and post-session SEQ-P ratings from the final sessions only. Pre-to-post session mood change was unrelated to outcome in both treatments. These results suggest different change processes are at work in CBT and PIT.