Semiparametric Odds Ratio Model And Its Applications

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Semiparametric Odds Ratio Model and Its Applications

Author : Hua Yun Chen
Publisher : CRC Press
ISBN 13 : 1351049739
Total Pages : 222 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Semiparametric Odds Ratio Model and Its Applications by : Hua Yun Chen

Download or read book Semiparametric Odds Ratio Model and Its Applications written by Hua Yun Chen and published by CRC Press. This book was released on 2021-12-20 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beginning with familiar models and moving onto advanced semiparametric modelling tools Semiparametric Odds Ratio Model and its Applications introduces readers to a new range of flexible statistical models and provides guidance on their application using real data examples. This books range of real-world examples and exploration of common statistical problems makes it an invaluable reference for research professionals and graduate students of biostatistics, statistics, and other quantitative fields. Key Features: Introduces flexible statistical models that have yet to systematically introduced in course materials. Discusses applications of the proposed modelling framework in several important statistical problems, ranging from biased sampling designs and missing data, graphical models, survival analysis, Gibbs sampler and model compatibility, and density estimation. Includes real data examples to demonstrate the use of the proposed models, and estimation and inference tools.

Semiparametric Odds Ratio Model and Its Applications

Author : Hua Yun Chen
Publisher : CRC Press
ISBN 13 : 1351049747
Total Pages : 296 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Semiparametric Odds Ratio Model and Its Applications by : Hua Yun Chen

Download or read book Semiparametric Odds Ratio Model and Its Applications written by Hua Yun Chen and published by CRC Press. This book was released on 2021-12-20 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beginning with familiar models and moving onto advanced semiparametric modelling tools Semiparametric Odds Ratio Model and its Applications introduces readers to a new range of flexible statistical models and provides guidance on their application using real data examples. This books range of real-world examples and exploration of common statistical problems makes it an invaluable reference for research professionals and graduate students of biostatistics, statistics, and other quantitative fields. Key Features: Introduces flexible statistical models that have yet to systematically introduced in course materials. Discusses applications of the proposed modelling framework in several important statistical problems, ranging from biased sampling designs and missing data, graphical models, survival analysis, Gibbs sampler and model compatibility, and density estimation. Includes real data examples to demonstrate the use of the proposed models, and estimation and inference tools.

Biased Sampling, Over-identified Parameter Problems and Beyond

Author : Jing Qin
Publisher : Springer
ISBN 13 : 9811048568
Total Pages : 626 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Biased Sampling, Over-identified Parameter Problems and Beyond by : Jing Qin

Download or read book Biased Sampling, Over-identified Parameter Problems and Beyond written by Jing Qin and published by Springer. This book was released on 2017-06-14 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others.

Flexible Imputation of Missing Data, Second Edition

Author : Stef van Buuren
Publisher : CRC Press
ISBN 13 : 0429960344
Total Pages : 329 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Flexible Imputation of Missing Data, Second Edition by : Stef van Buuren

Download or read book Flexible Imputation of Missing Data, Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

Monte-Carlo Simulation-Based Statistical Modeling

Author : Ding-Geng (Din) Chen
Publisher : Springer
ISBN 13 : 9811033072
Total Pages : 440 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Monte-Carlo Simulation-Based Statistical Modeling by : Ding-Geng (Din) Chen

Download or read book Monte-Carlo Simulation-Based Statistical Modeling written by Ding-Geng (Din) Chen and published by Springer. This book was released on 2017-02-01 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

Advanced Techniques for Modelling Maternal and Child Health in Africa

Author : Ngianga-Bakwin Kandala
Publisher : Springer Science & Business Media
ISBN 13 : 9400767781
Total Pages : 336 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Advanced Techniques for Modelling Maternal and Child Health in Africa by : Ngianga-Bakwin Kandala

Download or read book Advanced Techniques for Modelling Maternal and Child Health in Africa written by Ngianga-Bakwin Kandala and published by Springer Science & Business Media. This book was released on 2013-09-06 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents both theoretical contributions and empirical applications of advanced statistical techniques including geo-additive models that link individual measures with area variables to account for spatial correlation; multilevel models that address the issue of clustering within family and household; multi-process models that account for interdependencies over life-course events and non-random utilization of health services; and flexible parametric alternatives to existing intensity models. These analytical techniques are illustrated mainly through modeling maternal and child health in the African context, using data from demographic and health surveys. In the past, the estimation of levels, trends and differentials in demographic and health outcomes in developing countries was heavily reliant on indirect methods that were devised to suit limited or deficient data. In recent decades, world-wide surveys like the World Fertility Survey and its successor, the Demographic and Health Survey have played an important role in filling the gap in survey data from developing countries. Such modern demographic and health surveys enable investigators to make in-depth analyses that guide policy intervention strategies, and such analyses require the modern and advanced statistical techniques covered in this book. The text is ideally suited for academics, professionals, and decision makers in the social and health sciences, as well as others with an interest in statistical modelling, demographic and health surveys. Scientists and students in applied statistics, epidemiology, medicine, social and behavioural sciences will find it of value.

Semiparametric Approaches to Developing Models for Predicting Binary Outcomes Through Data and Information Integration

Author : Xinglei Chai
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Semiparametric Approaches to Developing Models for Predicting Binary Outcomes Through Data and Information Integration by : Xinglei Chai

Download or read book Semiparametric Approaches to Developing Models for Predicting Binary Outcomes Through Data and Information Integration written by Xinglei Chai and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We developed statistical methods for evaluating the added value of biomarkers for predicting binary outcomes when biomarker data has limited availability. In the first project, we considered a cost effective study design called "two-phase study," where data on the outcome and established risk predictors was collected for all study subjects in Phase I while biomarkers were measured only for a judiciously selected subset in Phase II. Using a logistic regression model to describe the relationship between the binary outcome and risk predictors, we developed three approaches to estimating the risk distribution and summary measures of predictive accuracy. We showed that all three estimators were consistent and asymptotically normally distributed, and compared the efficiency and robustness of the three methods through extensive simulation studies and application to an ongoing biomarker study of Gestational Diabetes. We also developed a novel sampling strategy for selecting Phase II subjects towards improved efficiency for estimating measures of predictive accuracy. In the second project, we developed a statistical method for alleviating the challenge of lack of independent data to validate biomarkers for prediction, focusing on model calibration. When a well-calibrated model with only standard predictors exists, we proposed to calibrate the new model to the existing model at the stage of model development. With data collected under a case-control study design, we developed a novel constrained maximum likelihood approach to fitting logistic regression models that brought this idea to fruition. We developed large sample theory for this method, and performed extensive simulation studies to assess the impact of constraints on the odds ratio parameter estimates. We applied our method to analyze a case-control study of breast cancer nested within the Breast Cancer Detection and Demonstration Project to evaluate the added value of mammographic density for predicting the 5-year risk of breast cancer. In the third project, we extended the statistical method developed in the second project to accommodate the cross-sectional study design. By simulation studies and the analysis of Gestational Diabetes, we demonstrated that our method ensured that the model was well calibrated.

Modeling Ordered Choices

Author : William H. Greene
Publisher : Cambridge University Press
ISBN 13 : 1139485954
Total Pages : 383 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Modeling Ordered Choices by : William H. Greene

Download or read book Modeling Ordered Choices written by William H. Greene and published by Cambridge University Press. This book was released on 2010-04-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.

Semiparametric Regression with R

Author : Jaroslaw Harezlak
Publisher : Springer
ISBN 13 : 1493988530
Total Pages : 331 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Semiparametric Regression with R by : Jaroslaw Harezlak

Download or read book Semiparametric Regression with R written by Jaroslaw Harezlak and published by Springer. This book was released on 2018-12-12 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R.

Regression Modeling Strategies

Author : Frank E. Harrell , Jr.
Publisher : Springer
ISBN 13 : 3319194259
Total Pages : 598 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Regression Modeling Strategies by : Frank E. Harrell , Jr.

Download or read book Regression Modeling Strategies written by Frank E. Harrell , Jr. and published by Springer. This book was released on 2015-08-14 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

Mixed Effects Models for Complex Data

Author : Lang Wu
Publisher : CRC Press
ISBN 13 : 9781420074086
Total Pages : 431 pages
Book Rating : 4.0/5 (74 download)

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Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu

Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Dissertation Abstracts International

Author :
Publisher :
ISBN 13 :
Total Pages : 810 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Dissertation Abstracts International by :

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2009 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Journal of the American Statistical Association

Author :
Publisher :
ISBN 13 :
Total Pages : 876 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Journal of the American Statistical Association by :

Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2008 with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Abstracts of Communications

Author :
Publisher :
ISBN 13 :
Total Pages : 472 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Abstracts of Communications by :

Download or read book Abstracts of Communications written by and published by . This book was released on 1998 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Modern Monte Carlo Methods and Their Application in Semiparametric Regression

Author : Samuel Joseph Thomas
Publisher :
ISBN 13 :
Total Pages : 208 pages
Book Rating : 4.:/5 (125 download)

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Book Synopsis Modern Monte Carlo Methods and Their Application in Semiparametric Regression by : Samuel Joseph Thomas

Download or read book Modern Monte Carlo Methods and Their Application in Semiparametric Regression written by Samuel Joseph Thomas and published by . This book was released on 2021 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essence of Bayesian data analysis is to ascertain posterior distributions. Posteriors generally do not have closed-form expressions for direct computation in practical applications. Analysts, therefore, resort to Markov Chain Monte Carlo (MCMC) methods for the generation of sample observations that approximate the desired posterior distribution. Standard MCMC methods simulate sample values from the desired posterior distribution via random proposals. As a result, the mechanism used to generate the proposals inevitably determines the efficiency of the algorithm. One of the modern MCMC techniques designed to explore the high-dimensional space more efficiently is Hamiltonian Monte Carlo (HMC), based on the Hamiltonian differential equations. Inspired by classical mechanics, these equations incorporate a latent variable to generate MCMC proposals that are likely to be accepted. This dissertation discusses how such a powerful computational approach can be used for implementing statistical models. Along this line, I created a unified computational procedure for using HMC to fit various types of statistical models. The procedure that I proposed can be applied to a broad class of models, including linear models, generalized linear models, mixed-effects models, and various types of semiparametric regression models. To facilitate the fitting of a diverse set of models, I incorporated new parameterization and decomposition schemes to ensure the numerical performance of Bayesian model fitting without sacrificing the procedure's general applicability. As a concrete application, I demonstrate how to use the proposed procedure to fit a multivariate generalized additive model (GAM), a nonstandard statistical model with a complex covariance structure and numerous parameters. Byproducts of the research include two software packages that all practical data analysts to use the proposed computational method to fit their own models. The research's main methodological contribution is the unified computational approach that it presents for Bayesian model fitting that can be used for standard and nonstandard statistical models. Availability of such a procedure has greatly enhanced statistical modelers' toolbox for implementing new and nonstandard statistical models.

Regression Modeling Strategies

Author : Frank E. Harrell
Publisher : Springer Science & Business Media
ISBN 13 : 147573462X
Total Pages : 583 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Regression Modeling Strategies by : Frank E. Harrell

Download or read book Regression Modeling Strategies written by Frank E. Harrell and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

The Statistical Evaluation of Medical Tests for Classification and Prediction

Author : Margaret Sullivan Pepe
Publisher : OUP Oxford
ISBN 13 : 019158861X
Total Pages : 319 pages
Book Rating : 4.1/5 (915 download)

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Book Synopsis The Statistical Evaluation of Medical Tests for Classification and Prediction by : Margaret Sullivan Pepe

Download or read book The Statistical Evaluation of Medical Tests for Classification and Prediction written by Margaret Sullivan Pepe and published by OUP Oxford. This book was released on 2003-03-13 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine.