Praise for the First Edition of Statistical Analysis with Missing Data " An important contribution to the applied statistics literature I give the book high marks for unifying & making accessible much of the past & current work in this important area" William E Strawderman Rutgers University " This bookprovides interesting real-life examples stimulating end-of-chapter exercises & up-to-date references It should be on every applied statistician s bookshelf" The Statistician " The book should be studied in the statistical methods department in every statistical agency" Journal of Official Statistics Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods Now reflecting extensive developments in Bayesian methods for simulating posterior distributions this Second Edition by two acknowledged experts on the subject offers a thoroughly up-to-date reorganized survey of current methodology for handling missing-data problems Blending theory & application authors Roderick Little & Donald Rubin review historical approaches to the subject & describe rigorous yet simple methods for multivariate analysis with missing values They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data & the missing-data mechanism & apply the theory to a wide range of important missing-data problems The new edition now enlarges its coverage to include Expanded coverage of Bayesian methodology both theoretical & computational & of multiple imputation Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data-generating & missing-data mechanisms Applications of the approach in a variety of contexts including regression factor analysis contingency table analysis time series & sample survey inference Extensive references examples & exercises Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue Statistical Analysis With Missing Data was among those chosen ...

Computational statistics & statistical computing are two areas that employ computational graphical & numerical approaches to solve statistical problems making the versatile R language an ideal computing environment for these fields One of the first books on these topics to feature R Statistical Computing with R covers the traditional core material of computational statistics with an emphasis on using the R language via an examples-based approach Suitable for an introductory course in computational statistics or for self-study it

Includes:: R code for all examples & R notes to help explain the R programming concepts After an overview of computational statistics & an introduction to the R computing environment the book reviews some basic concepts in probability & classical statistical inference Each subsequent chapter explores a specific topic in computational statistics These chapters cover the simulation of random variables from probability distributions the visualization of multivariate data Monte Carlo integration & variance reduction methods Monte Carlo methods in inference bootstrap & jackknife permutation tests Markov chain Monte Carlo (MCMC) methods & density estimation The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions Focusing on implementation rather than theory this text serves as a balanced accessible introduction to computational statistics & statistical computing

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Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts 3rd ed Designed for the second course in statistics it is one of the few texts that focuses just on intermediate statistics The book highlights how statistics work & what they mean to better prepare students to analyze their own data & interpret SPSS & research results As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data Determining appropriate sample sizes is emphasized throughout Only crucial equations are included The new edition features New co-author Debbie L Hahs-Vaughn the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award A new chapter on logistic regression models for today's more complex methodologies Much more on computing confidence intervals & conducting power analyses using GPower All new SPSS version 19 screenshots to help navigate through the program & annotated output to assist in the interpretation of results Sections on how to write-up statistical results in APA format & new templates for writing research questions New learning tools including chapter-opening vignettes outlines a list of key concepts Stop & Think boxes & many more examples tables & figures More tables of assumptions & the effects of their violation including how to test them in SPSS 33 new conceptual computational & all new interpretative problems A website with Power Points answers to the even-numbered problems detailed solutions to the odd-numbered problems & test items for instructors & for students the chapter outlines key concepts & datasets Each chapter begins with an outline a list of key concepts & a research vignette related to the concepts Realistic examples from education & the behavioral sciences illustrate those concepts Each example examines the procedures & assumptions & provides tips for how to run SPSS & develop an APA style write-up Tables of assumptions & the effects of their violation are included along with how to test assumptions in SPSS Each chapter

Includes:: computational conceptual & interpretive problems Answers to the odd-numbered problems are provided The SPSS data sets that correspond to the book's examples & problems are available on the web The book covers basic & advanced analysis of variance models & topics not dealt with in other texts such as robust methods multiple comparison & non-parametric procedures & multiple & logistic regression models Intended for courses in intermediate statistics andor statistics II taught in education andor the behavioral sciences predominantly at the master's or doctoral level Knowledge of introductory statistics is assumed

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Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore it is much easier to estimate & interpret than a dense model Statistical Learning with Sparsity The Lasso & Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data Top experts in this rapidly evolving field the authors describe the lasso for linear regression & a simple coordinate descent algorithm for its computation They discuss the application of 1 penalties to generalized linear models & support vector machines cover generalized penalties such as the elastic net & group lasso & review numerical methods for optimization They also present statistical inference methods for fitted (lasso) models including the bootstrap Bayesian methods & recently developed approaches In addition the book examines matrix decomposition sparse multivariate analysis graphical models & compressed sensing It concludes with a survey of theoretical results for the lasso In this age of big data the number of features measured on a person or object can be large & might be larger than the number of observations This book shows how the sparsity assumption allows us to tackle these problems & extract useful & reproducible patterns from big datasets Data analysts computer scientists & theorists will appreciate this thorough & up-to-date treatment of sparse statistical modeling ...

In each generation scientists must redefine their fields abstracting simplifying & distilling the previous standard topics to make room for new advances & methods Sethna's book takes this step for statistical mechanics
- a field rooted in physics & chemistry whose ideas & methods are now central to information theory complexity & modern biology Aimed at advanced undergraduates & early graduate students in all of these fields Sethna limits his main presentation to the topics that future mathematicians & biologists as well as physicists & chemists will find fascinating & central to their work The amazing breadth of the field is reflected in the author's large supply of carefully crafted exercises each an introduction to a whole field of study everything from chaos through information theory to life at the end of the universe
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Designing algorithms to recommend items such as news articles & movies to users is a challenging task in numerous web applications The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives Major technical challenges are high dimensional prediction with sparse data & constructing high dimensional sequential designs to collect data for user modeling & system design This comprehensive treatment of the statistical issues that arise in recommender systems

Includes:: detailed in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods) bilinear random-effects models (matrix factorization) & scalable model fitting using modern computing paradigms like Map Reduce The authors draw upon their vast experience working with such large-scale systems at Yahoo! & Linked In & bridge the gap between theory & practice by illustrating complex concepts with examples from applications they are directly involved with

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Statistical methods in modern research increasingly entail developing estimating & testing models for data Rather than rigid methods of data analysis the need today is for more flexible methods for modelling data In this logical easy-to-follow & exceptionally clear book David Flora provides a comprehensive survey of the major statistical procedures currently used His innovative model-based approach teaches you how to Understand & choose the right statistical model to fit your data Match substantive theory & statistical models Apply statistical procedures hands-on with example data analyses Develop & use graphs to understand data & fit models to data Work with statistical modeling principles using any software package Learn by applying with input & output files for R SAS SPSS & Mplus Statistical Methods for the Social & Behavioural Sciences A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics & begin using the right statistical modeling method for their own data It is particularly suited to second or advanced courses in statistical methods across the social & behavioural sciences ...

This book provides a general introduction to the ideas & methods of statistical mechanics with the principal aim of meeting the needs of Master's students in chemical mechanical & materials science engineering Extensive introductory information is presented on many general physics topics in which students in engineering are inadequately trained ranging from the Hamiltonian formulation of classical mechanics to basic quantum mechanics electromagnetic fields in matter intermolecular forces & transport phenomena Since engineers should be able to apply physical concepts the book also focuses on the practical applications of statistical physics to material science & to cutting-edge technologies with brief but informative sections on for example interfacial properties disperse systems nucleation magnetic materials superfluidity & ultralow temperature technologies The book adopts a graded approach to learning the opening four basic-level chapters being followed by advanced "starred" sections in which special topics are discussed Its relatively informal style including the use of musical metaphors to guide the reader through the text will aid self-learning ...

Statistical Rethinking A Bayesian Course with Examples in R & Stan builds readers' knowledge of & confidence in statistical modeling Reflecting the need for even minor programming in today's model-based statistics the book pushes readers to perform step-by-step calculations that are usually automated This unique computational approach ensures that readers understand enough of the details to make reasonable choices & interpretations in their own modeling work The text presents generalized linear multilevel models from a Bayesian perspective relying on a simple logical interpretation of Bayesian probability & maximum entropy It covers from the basics of regression to multilevel models The author also discusses measurement error missing data & Gaussian process models for spatial & network autocorrelation By using complete R code examples throughout this book provides a practical foundation for performing statistical inference Designed for both Ph D students & seasoned professionals in the natural & social sciences it prepares them for more advanced or specialized statistical modeling Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website & Git Hub The two core functions (map & map 2stan) of this package allow a variety of statistical models to be constructed from standard model formulas ...

Fully endorsed by OCR & revised to match the 2005

Specification this series has been carefully revised by experienced

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