High Dimensional Variable Selection For Glms And Survival Models

Download High Dimensional Variable Selection For Glms And Survival Models full books in PDF, epub, and Kindle. Read online High Dimensional Variable Selection For Glms And Survival Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Computational Intelligence Methods for Bioinformatics and Biostatistics

Author : Maria Raposo
Publisher : Springer Nature
ISBN 13 : 3030345858
Total Pages : 334 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Computational Intelligence Methods for Bioinformatics and Biostatistics by : Maria Raposo

Download or read book Computational Intelligence Methods for Bioinformatics and Biostatistics written by Maria Raposo and published by Springer Nature. This book was released on 2020-01-22 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 15th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics., CIBB 2018, held in Caparica, Portugal, in September 2018. The 32 revised full papers were carefully reviewed and selected from 51 submissions. The papers present current trends at the edge of computer and life sciences, the application of computational intelligence to a system and synthetic biology and the consequent impact on innovative medicine were presented. Theoretical and experimental biologists also presented novel challenges and fostered multidisciplinary collaboration aiming to blend theory and practice, where the founding theories of the techniques used for modelling and analyzing biological systems are investigated and used for practical applications and the supporting technologies.

Foundations of Linear and Generalized Linear Models

Author : Alan Agresti
Publisher : John Wiley & Sons
ISBN 13 : 1118730054
Total Pages : 471 pages
Book Rating : 4.1/5 (187 download)

DOWNLOAD NOW!

Book Synopsis Foundations of Linear and Generalized Linear Models by : Alan Agresti

Download or read book Foundations of Linear and Generalized Linear Models written by Alan Agresti and published by John Wiley & Sons. This book was released on 2015-01-15 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Handbook of Bayesian Variable Selection

Author : Mahlet G. Tadesse
Publisher : CRC Press
ISBN 13 : 1000510204
Total Pages : 491 pages
Book Rating : 4.0/5 (5 download)

DOWNLOAD NOW!

Book Synopsis Handbook of Bayesian Variable Selection by : Mahlet G. Tadesse

Download or read book Handbook of Bayesian Variable Selection written by Mahlet G. Tadesse and published by CRC Press. This book was released on 2021-12-24 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions. Features: Provides a comprehensive review of methods and applications of Bayesian variable selection. Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection. Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement. Includes contributions by experts in the field. Supported by a website with code, data, and other supplementary material

Random Effect and Latent Variable Model Selection

Author : David Dunson
Publisher : Springer Science & Business Media
ISBN 13 : 0387767215
Total Pages : 174 pages
Book Rating : 4.3/5 (877 download)

DOWNLOAD NOW!

Book Synopsis Random Effect and Latent Variable Model Selection by : David Dunson

Download or read book Random Effect and Latent Variable Model Selection written by David Dunson and published by Springer Science & Business Media. This book was released on 2010-03-18 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.

Multivariable Model - Building

Author : Patrick Royston
Publisher : John Wiley & Sons
ISBN 13 : 9780470770788
Total Pages : 322 pages
Book Rating : 4.7/5 (77 download)

DOWNLOAD NOW!

Book Synopsis Multivariable Model - Building by : Patrick Royston

Download or read book Multivariable Model - Building written by Patrick Royston and published by John Wiley & Sons. This book was released on 2008-09-15 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approach, developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models.

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)

DOWNLOAD NOW!

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.

Statistics and Machine Learning Methods for EHR Data

Author : Hulin Wu
Publisher : CRC Press
ISBN 13 : 1000260968
Total Pages : 268 pages
Book Rating : 4.0/5 (2 download)

DOWNLOAD NOW!

Book Synopsis Statistics and Machine Learning Methods for EHR Data by : Hulin Wu

Download or read book Statistics and Machine Learning Methods for EHR Data written by Hulin Wu and published by CRC Press. This book was released on 2020-12-10 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

Applying Generalized Linear Models

Author : James K. Lindsey
Publisher : Springer Science & Business Media
ISBN 13 : 038722730X
Total Pages : 265 pages
Book Rating : 4.3/5 (872 download)

DOWNLOAD NOW!

Book Synopsis Applying Generalized Linear Models by : James K. Lindsey

Download or read book Applying Generalized Linear Models written by James K. Lindsey and published by Springer Science & Business Media. This book was released on 2008-01-15 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Generalized Additive Models

Author : Simon Wood
Publisher : CRC Press
ISBN 13 : 1584884746
Total Pages : 412 pages
Book Rating : 4.5/5 (848 download)

DOWNLOAD NOW!

Book Synopsis Generalized Additive Models by : Simon Wood

Download or read book Generalized Additive Models written by Simon Wood and published by CRC Press. This book was released on 2006-02-27 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.

Nonparametric Statistics

Author : Michele La Rocca
Publisher : Springer Nature
ISBN 13 : 3030573060
Total Pages : 547 pages
Book Rating : 4.0/5 (35 download)

DOWNLOAD NOW!

Book Synopsis Nonparametric Statistics by : Michele La Rocca

Download or read book Nonparametric Statistics written by Michele La Rocca and published by Springer Nature. This book was released on 2020-11-11 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018. It covers theory, methodology, applications and computational aspects, addressing topics such as nonparametric curve estimation, regression smoothing, models for time series and more generally dependent data, varying coefficient models, symmetry testing, robust estimation, and rank-based methods for factorial design. It also discusses nonparametric and permutation solutions for several different types of data, including ordinal data, spatial data, survival data and the joint modeling of both longitudinal and time-to-event data, permutation and resampling techniques, and practical applications of nonparametric statistics. The International Society for Nonparametric Statistics is a unique global organization, and its international conferences are intended to foster the exchange of ideas and the latest advances and trends among researchers from around the world and to develop and disseminate nonparametric statistics knowledge. The ISNPS 2018 conference in Salerno was organized with the support of the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and the University of Salerno.

Statistical Learning with Sparsity

Author : Trevor Hastie
Publisher : CRC Press
ISBN 13 : 1498712177
Total Pages : 354 pages
Book Rating : 4.4/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Statistical Learning with Sparsity by : Trevor Hastie

Download or read book Statistical Learning with Sparsity written by Trevor Hastie and published by CRC Press. This book was released on 2015-05-07 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

Foundations of Linear and Generalized Linear Models

Author : Alan Agresti
Publisher : John Wiley & Sons
ISBN 13 : 1118730038
Total Pages : 471 pages
Book Rating : 4.1/5 (187 download)

DOWNLOAD NOW!

Book Synopsis Foundations of Linear and Generalized Linear Models by : Alan Agresti

Download or read book Foundations of Linear and Generalized Linear Models written by Alan Agresti and published by John Wiley & Sons. This book was released on 2015-02-23 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Models for Data Analysis

Author : Eugenio Brentari
Publisher : Springer Nature
ISBN 13 : 3031158857
Total Pages : 320 pages
Book Rating : 4.0/5 (311 download)

DOWNLOAD NOW!

Book Synopsis Models for Data Analysis by : Eugenio Brentari

Download or read book Models for Data Analysis written by Eugenio Brentari and published by Springer Nature. This book was released on 2023-02-20 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 49Th Scientific meeting of the Italian Statistical Society was held in June 2018 in Palermo, with more than 450 attendants. There were plenary sessions as well as specialized and solicited and contributed sessions. This volume collects a selection of twenty extended contributions covering a wide area of applied and theoretical issues, according to the modern trends in statistical sciences. Only to mention some topics, there are papers on modern textual analysis, sensorial analysis, social inequalities, themes on demography, modern modeling of functional data and high dimensional data, and many other topics. This volume is addressed to academics, PhD students, professionals and researchers in applied and theoretical statistical models for data analysis.

Generalized Linear Models for Insurance Data

Author : Piet de Jong
Publisher : Cambridge University Press
ISBN 13 : 1139470477
Total Pages : 207 pages
Book Rating : 4.1/5 (394 download)

DOWNLOAD NOW!

Book Synopsis Generalized Linear Models for Insurance Data by : Piet de Jong

Download or read book Generalized Linear Models for Insurance Data written by Piet de Jong and published by Cambridge University Press. This book was released on 2008-02-28 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

COMPSTAT 2008

Author : Paula Brito
Publisher : Springer Science & Business Media
ISBN 13 : 3790820849
Total Pages : 557 pages
Book Rating : 4.7/5 (98 download)

DOWNLOAD NOW!

Book Synopsis COMPSTAT 2008 by : Paula Brito

Download or read book COMPSTAT 2008 written by Paula Brito and published by Springer Science & Business Media. This book was released on 2008-08-11 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: 18th Symposium Held in Porto, Portugal, 2008

Large Dimensional Factor Analysis

Author : Jushan Bai
Publisher : Now Publishers Inc
ISBN 13 : 1601981449
Total Pages : 90 pages
Book Rating : 4.6/5 (19 download)

DOWNLOAD NOW!

Book Synopsis Large Dimensional Factor Analysis by : Jushan Bai

Download or read book Large Dimensional Factor Analysis written by Jushan Bai and published by Now Publishers Inc. This book was released on 2008 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.

An Introduction to Categorical Data Analysis

Author : Alan Agresti
Publisher : John Wiley & Sons
ISBN 13 : 1119405270
Total Pages : 393 pages
Book Rating : 4.1/5 (194 download)

DOWNLOAD NOW!

Book Synopsis An Introduction to Categorical Data Analysis by : Alan Agresti

Download or read book An Introduction to Categorical Data Analysis written by Alan Agresti and published by John Wiley & Sons. This book was released on 2018-10-11 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.