Data Analysis Using Hierarchical Generalized Linear Models With R
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Data Analysis Using Hierarchical Generalized Linear Models With R
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Author : Youngjo Lee
language : en
Publisher:
Release Date : 2020
Data Analysis Using Hierarchical Generalized Linear Models With R written by Youngjo Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Data Analysis Using Hierarchical Generalized Linear Models With R
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Author : Youngjo Lee
language : en
Publisher: CRC Press
Release Date : 2017-07-06
Data Analysis Using Hierarchical Generalized Linear Models With R written by Youngjo Lee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-06 with Mathematics categories.
Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
Data Analysis Using Hierarchical Generalized Linear Models With R
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Author : Youngjo Lee
language : en
Publisher: CRC Press
Release Date : 2017-07-06
Data Analysis Using Hierarchical Generalized Linear Models With R written by Youngjo Lee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-06 with Mathematics categories.
Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.
Generalized Linear Models With Random Effects
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Author : Youngjo Lee
language : en
Publisher: CRC Press
Release Date : 2018-07-11
Generalized Linear Models With Random Effects written by Youngjo Lee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-11 with Mathematics categories.
This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.
Statistical Modeling With R
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Author : Pablo Inchausti
language : en
Publisher: Oxford University Press
Release Date : 2023-01-16
Statistical Modeling With R written by Pablo Inchausti and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-16 with Science categories.
To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.
Advanced Regression Models With Sas And R
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Author : Olga Korosteleva
language : en
Publisher: CRC Press
Release Date : 2018-12-07
Advanced Regression Models With Sas And R written by Olga Korosteleva and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-07 with Mathematics categories.
Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression, the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors. Features: Presents the theoretical framework for each regression. Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical. Uses examples based on real-life consulting projects. Provides complete SAS and R codes for each example. Includes several exercises for every regression. Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required. The Author: Olga Korosteleva is a Professor of Statistics at California State University, Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years, she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences, nursing, kinesiology, and other fields.
Proceedings Of The Pacific Rim Statistical Conference For Production Engineering
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Author : Dongseok Choi
language : en
Publisher: Springer
Release Date : 2018-03-27
Proceedings Of The Pacific Rim Statistical Conference For Production Engineering written by Dongseok Choi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-27 with Mathematics categories.
This book presents the proceedings of the 2nd Pacific Rim Statistical Conference for Production Engineering: Production Engineering, Big Data and Statistics, which took place at Seoul National University in Seoul, Korea in December, 2016. The papers included discuss a wide range of statistical challenges, methods and applications for big data in production engineering, and introduce recent advances in relevant statistical methods.
The Oxford Handbook Of Political Methodology
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Author : Janet M. Box-Steffensmeier
language : en
Publisher: Oxford University Press
Release Date : 2008-08-21
The Oxford Handbook Of Political Methodology written by Janet M. Box-Steffensmeier and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-21 with Political Science categories.
Political methodology has changed dramatically over the past thirty years, and many new methods and techniques have been developed. Both the Political Methodology Society and the Qualitative/Multi-Methods Section of the American Political Science Association have engaged in ongoing research and training programs that have advanced quantitative and qualitative methodology. The Oxford Handbook of Political Methodology presents and synthesizes these developments. The Handbook provides comprehensive overviews of diverse methodological approaches, with an emphasis on three major themes. First, specific methodological tools should be at the service of improved conceptualization, comprehension of meaning, measurement, and data collection. They should increase analysts' leverage in reasoning about causal relationships and evaluating them empirically by contributing to powerful research designs. Second, the authors explore the many different ways of addressing these tasks: through case-studies and large-n designs, with both quantitative and qualitative data, and via techniques ranging from statistical modelling to process tracing. Finally, techniques can cut across traditional methodological boundaries and can be useful for many different kinds of researchers. Many of the authors thus explore how their methods can inform, and be used by, scholars engaged in diverse branches of methodology.
The Sage Handbook Of Multilevel Modeling
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Author : Marc A. Scott
language : en
Publisher: SAGE
Release Date : 2013-08-31
The Sage Handbook Of Multilevel Modeling written by Marc A. Scott and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-31 with Social Science categories.
In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Dynamic Mixed Models For Familial Longitudinal Data
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Author : Brajendra C. Sutradhar
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-01-27
Dynamic Mixed Models For Familial Longitudinal Data written by Brajendra C. Sutradhar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-27 with Mathematics categories.
This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University ResearchProfessor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the society including his special services for society’s annual meetings.