Generalized, Linear, and Mixed Models 2e John Wiley & Sons Inc

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An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects. A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide comprehensive coverage of the latest statistical models for correlated, non-normally distributed data.Thoroughly updated to reflect the latest developments in the field, the Second Edition features: A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation A new chapter that treats shared random effects models, latent class models, and properties of models A revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions Expanded coverage of marginal versus conditional models Numerous new and updated examples With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models , Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers.

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Repeated Measures Design With Generalized Linear Mixed Models For Randomized Controlled Trials

Książki Obcojęzyczne>Angielskie>Medicine>Medicine: general issues>Public health & preventive medicine>Epidemiology & medical statistics...

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Direct Likelihood Approximations For Generalized Linear Mixed Models

Książki Obcojęzyczne>Angielskie>Mathematics & science>Mathematics>Probability & statistics

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Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials

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Vector Generalized Linear And Additive Models

Książki Obcojęzyczne>Angielskie>Computing & information technology>Business applications>Mathematical & statistical softwareKsiążki Obcoję...

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Linear Mixed-Effects Models Using R Springer

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Connecting theory, software and applications, this book provides state-of-the-art descriptions of the implementation of LMMs in R, showing their application in such fields as biostatistics, public health, psychometrics, educational measurement and sociology.

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Linear Mixed-Effects Models Using R Springer-Verlag New York Inc.

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Connecting theory, software and applications, this book provides state-of-the-art descriptions of the implementation of LMMs in R, showing their application in such fields as biostatistics, public health, psychometrics, educational measurement and sociology.

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Generalized Additive Models Taylor & Francis Inc

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The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book

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Advanced R Statistical Programming and Data Models APress

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Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You'll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).

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Statistical Foundations of Actuarial Learning and its Applications Springer, Berlin

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This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features.  Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.

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New Trends in Nanotechnology and Fractional Calculus Springer Verlag

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In recent years fractional calculus has played an important role in various fields such as mechanics, electricity, chemistry, biology, economics, modeling, identification, control theory and signal processing. The scope of this book is to present the state of the art in the study of fractional systems and the application of fractional differentiation. Furthermore, the manufacture of nanowires is important for the design of nanosensors and the development of high-yield thin films is vital in procuring clean solar energy. This wide range of applications is of interest to engineers, physicists and mathematicians.Table of contentsPreface;New trends in nanotechnology: Novel Molecular Diodes Developed by Chemial Conjugation of Carbon Nanotubes with Peptide Nucleic Acid, by Krishna V. Singh, Miroslav Penchev, Xiaoye Jing, Alfredo A. Martinez-Morales, Cengiz S. Ozkan and Mihri Ozkan;Hybrid Single Walled Carbon Nanotube FETs for High Fidelity DNA Detection, by Xu Wang, Mihri Ozkan, Gurer Budak, Ziya B. Guvenc and Cengiz S. Ozkan;Towards integrated nanoelectronic and photonic devices, by Alexander Quandt, Maurizio Ferrari and Giancarlo C. Righini;New noninvasive methods for

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Effective Statistical Learning Methods for Actuaries I Springer Nature Switzerland AG

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This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries . Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

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Categorical Data Analysis, 3e John Wiley & Sons Inc

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Praise for the Second Edition§"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."§--Statistics in Medicine§"It is a total delight reading this book."§--Pharmaceutical Research§"If you do any analysis of categorical data, this is an essential desktop reference."§--Technometrics§The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.§Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:§An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models§Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis§New sections introducing the Bayesian approach for methods in that chapter§More than 100 analyses of data sets and over 600 exercises§Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources§A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions§Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

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Stochastic Calculus for Fractional Brownian Motion and Related Processes Springer, Berlin

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The theory of fractional Brownian motion and other long-memory processes are addressed in this volume. Interesting topics for PhD students and specialists in probability theory, stochastic analysis and financial mathematics demonstrate the modern level of this field. Among these are results about Levy characterization of fractional Brownian motion, maximal moment inequalities for Wiener integrals including the values 0

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