Marginal Models In Analysis Of Correlated Binary Data With Time Dependent Covariates
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Marginal Models In Analysis Of Correlated Binary Data With Time Dependent Covariates
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Author : Jeffrey R. Wilson
language : en
Publisher: Springer Nature
Release Date : 2020-09-28
Marginal Models In Analysis Of Correlated Binary Data With Time Dependent Covariates written by Jeffrey R. Wilson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-28 with Medical categories.
This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health.
Innovative Statistical Methods For Public Health Data
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Author : Ding-Geng (Din) Chen
language : en
Publisher: Springer
Release Date : 2015-08-31
Innovative Statistical Methods For Public Health Data written by Ding-Geng (Din) Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-31 with Medical categories.
The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference and it can be used in graduate level classes.
Modeling Binary Correlated Responses Using Sas Spss And R
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Author : Jeffrey R. Wilson
language : en
Publisher: Springer
Release Date : 2015-10-12
Modeling Binary Correlated Responses Using Sas Spss And R written by Jeffrey R. Wilson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-12 with Mathematics categories.
Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
Applied Machine Learning For Data Science Practitioners
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Author : Vidya Subramanian
language : en
Publisher: John Wiley & Sons
Release Date : 2025-05-28
Applied Machine Learning For Data Science Practitioners written by Vidya Subramanian and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-28 with Mathematics categories.
Single volume reference on using various aspects of data science to evaluate, understand, and solve business problems A reference book for anyone in the field of data science, Applied Machine Learning for Data Science Practitioners walks readers through the end-to-end process of solving any machine learning problem by identifying, choosing, and applying the right solution for the issue at hand. The text enables readers to figure out optimal validation techniques based on the use case and data orientation, choose a range of pertinent models from different types of learning, and score models to apply metrics across all the estimators evaluated. Unlike most books on data science in today's market that jump right into algorithms and coding and focus on the most-used algorithms, this text helps data scientists evaluate all pertinent techniques and algorithms to assess all these machine learning problems and suitable solutions. Readers can make an informed decision on which models and validation techniques to use based on the business problem, data availability, desired outcome, and more. Written by an internationally recognized author in the field of data science, Applied Machine Learning for Data Science Practitioners also covers topics such as: Data preparation, including basic data cleaning, integration, transformation, and compression methods, along with data visualization and exploratory analyses Cross-validation in model validation techniques, including independent, identically distributed, imbalanced, blocked, and grouped data Prediction using regression models and classification using classification models, with applicable performance measurements for each Types of clustering in clustering models based on partition, hierarchy, fuzzy theory, distribution, density, and graph theory Detecting anomalies, including types of anomalies and key terms like noise, rare events, and outliers Applied Machine Learning for Data Science Practitioners is an essential resource for all data scientists and business professionals to cross-validate a range of different algorithms to find an optimal solution. Readers are assumed to have a basic understanding of solving business problems using data, high school level math, statistics, and coding skills.
Statistical Regression Modeling With R
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Author : Ding-Geng (Din) Chen
language : en
Publisher: Springer Nature
Release Date : 2021-04-08
Statistical Regression Modeling With R written by Ding-Geng (Din) Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-08 with Mathematics categories.
This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.
Modeling Binary Correlated Responses
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Author : Jeffrey R. Wilson
language : en
Publisher: Springer Nature
Release Date : 2024-09-14
Modeling Binary Correlated Responses written by Jeffrey R. Wilson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-14 with Mathematics categories.
This book is an updated edition of Modeling Binary Correlated Responses Using SAS, SPSS and R, and now it includes the use of STATA. It uses these Statistical tools to analyze correlated binary data, accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages, as well as showcase both traditional and new methods for application to health-related research. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Short tutorials are in the appendix, for readers interested in learning more about the languages. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, SPSS and STATA, allows for easy implementation by readers. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
Progress In Applied Mathematical Modeling
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Author : Fengshan Yang
language : en
Publisher: Nova Publishers
Release Date : 2008
Progress In Applied Mathematical Modeling written by Fengshan Yang and has been published by Nova Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Mathematics categories.
This book presents new research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems. It includes heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimisation; finite volume, finite element, and boundary element procedures; decision sciences in an industrial and manufacturing context; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering.
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.
Generalized Estimating Equations
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Author : Andreas Ziegler
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-17
Generalized Estimating Equations written by Andreas Ziegler 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 2011-06-17 with Mathematics categories.
Generalized estimating equations have become increasingly popular in biometrical, econometrical, and psychometrical applications because they overcome the classical assumptions of statistics, i.e. independence and normality, which are too restrictive for many problems. Therefore, the main goal of this book is to give a systematic presentation of the original generalized estimating equations (GEE) and some of its further developments. Subsequently, the emphasis is put on the unification of various GEE approaches. This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM). The author details the statistical foundation of the GEE approach using more general estimation techniques. The book could therefore be used as basis for a course to graduate students in statistics, biostatistics, or econometrics, and will be useful to practitioners in the same fields.
Computational And Methodological Statistics And Biostatistics
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Author : Andriëtte Bekker
language : en
Publisher: Springer Nature
Release Date : 2020-08-10
Computational And Methodological Statistics And Biostatistics written by Andriëtte Bekker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-10 with Medical categories.
In the statistical domain, certain topics have received considerable attention during the last decade or so, necessitated by the growth and evolution of data and theoretical challenges. This growth has invariably been accompanied by computational advancement, which has presented end users as well as researchers with the necessary opportunities to handle data and implement modelling solutions for statistical purposes. Showcasing the interplay among a variety of disciplines, this book offers pioneering theoretical and applied solutions to practice-oriented problems. As a carefully curated collection of prominent international thought leaders, it fosters collaboration between statisticians and biostatisticians and provides an array of thought processes and tools to its readers. The book thereby creates an understanding and appreciation of recent developments as well as an implementation of these contributions within the broader framework of both academia and industry. Computational and Methodological Statistics and Biostatistics is composed of three main themes: • Recent developments in theory and applications of statistical distributions;• Recent developments in supervised and unsupervised modelling;• Recent developments in biostatistics; and also features programming code and accompanying algorithms to enable readers to replicate and implement methodologies. Therefore, this monograph provides a concise point of reference for a variety of current trends and topics within the statistical domain. With interdisciplinary appeal, it will be useful to researchers, graduate students, and practitioners in statistics, biostatistics, clinical methodology, geology, data science, and actuarial science, amongst others.