Data Analysis Using Regression and Multilevel/Hierarchical Models

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Author: Andrew Gelman,Jennifer Hill

Publisher: Cambridge University Press

ISBN: 9780521686891

Category: Mathematics

Page: 625

View: 2286

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS

Volume 1:Prelude and Static Models

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Author: Marc Kery,J. Andrew Royle

Publisher: Academic Press

ISBN: 0128014865

Category: Nature

Page: 808

View: 2105

Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection Presents models and methods for identifying unmarked individuals and species Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses Includes companion website containing data sets, code, solutions to exercises, and further information

The SAGE Handbook of Multilevel Modeling

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Author: Marc A. Scott,Jeffrey S. Simonoff,Brian D. Marx

Publisher: SAGE

ISBN: 1446265978

Category: Reference

Page: 696

View: 3899

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.

Applied Regression Modeling

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Author: Iain Pardoe

Publisher: John Wiley & Sons

ISBN: 1118345045

Category: Mathematics

Page: 346

View: 5202

Praise for the First Edition "The attention to detail is impressive. The book is very wellwritten and the author is extremely careful with his descriptions .. . the examples are wonderful." —The AmericanStatistician Fully revised to reflect the latest methodologies and emergingapplications, Applied Regression Modeling, Second Editioncontinues to highlight the benefits of statistical methods,specifically regression analysis and modeling, for understanding,analyzing, and interpreting multivariate data in business, science,and social science applications. The author utilizes a bounty of real-life examples, casestudies, illustrations, and graphics to introduce readers to theworld of regression analysis using various software packages,including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear andcareful writing style, the book introduces modeling extensions thatillustrate more advanced regression techniques, including logisticregression, Poisson regression, discrete choice models, multilevelmodels, and Bayesian modeling. In addition, the Second Edition features clarificationand expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated andadditional problems are included at the end of each chapter,allowing readers to test their comprehension of the presentedmaterial. In addition, a related website features the book'sdatasets, presentation slides, detailed statistical softwareinstructions, and learning resources including additional problemsand instructional videos. With an intuitive approach that is not heavy on mathematicaldetail, Applied Regression Modeling, Second Edition is anexcellent book for courses on statistical regression analysis atthe upper-undergraduate and graduate level. The book also serves asa valuable resource for professionals and researchers who utilizestatistical methods for decision-making in their everyday work.

Quantitative Genetics in the Wild

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Author: Anne Charmantier,Dany Garant,Loeske E. B. Kruuk

Publisher: OUP Oxford

ISBN: 0191655961

Category: Science

Page: 288

View: 2407

Although the field of quantitative genetics - the study of the genetic basis of variation in quantitative characteristics such as body size, or reproductive success - is almost 100 years old, its application to the study of evolutionary processes in wild populations has expanded greatly over the last few decades. During this time, the use of 'wild quantitative genetics' has provided insights into a range of important questions in evolutionary ecology, ranging from studies conducting research in well-established fields such as life-history theory, behavioural ecology and sexual selection, to others addressing relatively new issues such as populations' responses to climate change or the process of senescence in natural environments. Across these fields, there is increasing appreciation of the need to quantify the genetic - rather than just the phenotypic - basis and diversity of key traits, the genetic basis of the associations between traits, and the interaction between these genetic effects and the environment. This research activity has been fuelled by methodological advances in both molecular genetics and statistics, as well as by exciting results emerging from laboratory studies of evolutionary quantitative genetics, and the increasing availability of suitable long-term datasets collected in natural populations, especially in animals. Quantitative Genetics in the Wild is the first book to synthesize the current level of knowledge in this exciting and rapidly-expanding area. This comprehensive volume also offers exciting perspectives for future studies in emerging areas, including the application of quantitative genetics to plants or arthropods, unraveling the molecular basis of variation in quantitative traits, or estimating non-additive genetic variance. Since this book deals with many fundamental questions in evolutionary ecology, it should be of interest to graduate, post-graduate students, and academics from a wide array of fields such as animal behaviour, ecology, evolution, and genetics.

Causal Inference for Statistics, Social, and Biomedical Sciences

An Introduction

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Author: Guido W. Imbens,Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1316094391

Category: Mathematics

Page: N.A

View: 3048

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

The Analysis of Covariance and Alternatives

Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies

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Author: Bradley Huitema

Publisher: John Wiley & Sons

ISBN: 9781118067468

Category: Mathematics

Page: 480

View: 6421

A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.

Epidemiologic Methods

Studying the Occurrence of Illness

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Author: Noel S. Weiss,Thomas D. Koepsell

Publisher: Oxford University Press

ISBN: 0199387788

Category: Medical

Page: 560

View: 1353

This second edition of Epidemiologic Methods offers a rigorous introduction to the concepts and tools of epidemiologic research. Aimed chiefly at future epidemiologists, the book offers clear descriptions, practical examples, and question/answer sections for each of the science's key concepts. Authored by two award-winning epidemiology instructors, this book is ideally suited for use as a text in a graduate-level course sequence in epidemiologic methods. The book's chapters are organized around three main themes: general concepts and tools of epidemiology; major study designs; and special topics, including screening, outbreak investigations, and use of epidemiology to evaluate policies and programs. With additional exercises at the end of each chapter and expanded attention to topics such as confounding, this new edition of Epidemiologic Methods is an indispensable resource for the next generation of epidemiologic study.

Handbook of Markov Chain Monte Carlo

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Author: Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng

Publisher: CRC Press

ISBN: 1420079425

Category: Mathematics

Page: 619

View: 8354

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory. The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology. The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.