Download or read book Bayesian Analysis with Stata written by John Thompson and published by . This book was released on 2014-05-06 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Analysis with Stata is a compendium of Stata user-written commands for Bayesian analysis.
Download or read book An Introduction to Medical Statistics written by Martin Bland and published by Oxford University Press. This book was released on 2015-07-23 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its Fourth Edition, An Introduction to Medical Statistics continues to be a 'must-have' textbook for anyone who needs a clear logical guide to the subject. Written in an easy-to-understand style and packed with real life examples, the text clearly explains the statistical principles used in the medical literature. Taking readers through the common statistical methods seen in published research and guidelines, the text focuses on how to interpret and analyse statistics for clinical practice. Using extracts from real studies, the author illustrates how data can be employed correctly and incorrectly in medical research helping readers to evaluate the statistics they encounter and appropriately implement findings in clinical practice. End of chapter exercises, case studies and multiple choice questions help readers to apply their learning and develop their own interpretative skills. This thoroughly revised edition includes new chapters on meta-analysis, missing data, and survival analysis.
Download or read book Analysis of Incidence Rates written by Peter Cummings and published by CRC Press. This book was released on 2019-04-16 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incidence rates are counts divided by person-time; mortality rates are a well-known example. Analysis of Incidence Rates offers a detailed discussion of the practical aspects of analyzing incidence rates. Important pitfalls and areas of controversy are discussed. The text is aimed at graduate students, researchers, and analysts in the disciplines of epidemiology, biostatistics, social sciences, economics, and psychology. Features: Compares and contrasts incidence rates with risks, odds, and hazards. Shows stratified methods, including standardization, inverse-variance weighting, and Mantel-Haenszel methods Describes Poisson regression methods for adjusted rate ratios and rate differences. Examines linear regression for rate differences with an emphasis on common problems. Gives methods for correcting confidence intervals. Illustrates problems related to collapsibility. Explores extensions of count models for rates, including negative binomial regression, methods for clustered data, and the analysis of longitudinal data. Also, reviews controversies and limitations. Presents matched cohort methods in detail. Gives marginal methods for converting adjusted rate ratios to rate differences, and vice versa. Demonstrates instrumental variable methods. Compares Poisson regression with the Cox proportional hazards model. Also, introduces Royston-Parmar models. All data and analyses are in online Stata files which readers can download. Peter Cummings is Professor Emeritus, Department of Epidemiology, School of Public Health, University of Washington, Seattle WA. His research was primarily in the field of injuries. He used matched cohort methods to estimate how the use of seat belts and presence of airbags were related to death in a traffic crash. He is author or co-author of over 100 peer-reviewed articles.
Download or read book Introduction to Bayesian Statistics written by William M. Bolstad and published by John Wiley & Sons. This book was released on 2016-09-02 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: "...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.
Download or read book Applied Meta Analysis with R and Stata written by Ding-Geng (Din) Chen and published by CRC Press. This book was released on 2021-03-31 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis... A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.
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.
Download or read book Bayesian Statistics for Beginners written by Therese M. Donovan and published by Oxford University Press, USA. This book was released on 2019 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.
Download or read book A First Course in Bayesian Statistical Methods written by Peter D. Hoff and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Download or read book Generalized Linear Models and Extensions Second Edition written by James W. Hardin and published by Stata Press. This book was released on 2007 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deftly balancing theory and application, this book stands out in its coverage of the derivation of the GLM families and their foremost links. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, and more.
Download or read book Statistical Rethinking written by Richard McElreath and published by CRC Press. This book was released on 2018-01-03 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and 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 and 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 and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and 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 and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Download or read book Linear Mixed Models written by Brady T. West and published by CRC Press. This book was released on 2014-07-17 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations Use of the lmer() function in the lme4 R package New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs Updated graphical procedures in the software packages Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options More practical recommendations on using the software for analysis A new R package (WWGbook) that contains all of the data sets used in the examples Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.
Download or read book Quantitative Social Science written by Kosuke Imai and published by Princeton University Press. This book was released on 2021-03-16 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--
Download or read book Financial Econometrics Using Stata written by Simona Boffelli and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples.
Download or read book Data Analysis Using Regression and Multilevel Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Download or read book Survey Weights written by Richard Valliant and published by . This book was released on 2018 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Survey Weights: A Step-by-Step Guide to Calculation is the first guide geared toward Stata users that systematically covers the major steps taken in creating survey weights. These weights are used to project a sample to some larger population and can be computed for either probability or nonprobability samples. Sample designs can range from simple, single-stage samples to more complex, multistage samples, each of which may use specialized steps in weighting to account for selection probabilities, nonresponse, inaccurate coverage of a population by a sample, and auxiliary data to improve precision and compensate for coverage errors. The authors provide many examples with Stata code.
Download or read book A Visual Guide to Stata Graphics Second Edition written by Michael N. Mitchell and published by Stata Press. This book was released on 2008-06-04 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Power of Stata Graphics at Your Fingertips Whether you are new to Stata graphics or a seasoned veteran, this book teaches you how to use Stata to make high-quality graphs that stand out and enhance statistical results. With over 900 illustrated examples and quick-reference tabs, it offers a guide to creating and customizing graphs for any type of statistical data using either Stata commands or the Graph Editor. The author displays each graph example in full color with simple and clear instructions. He shows how to produce various types of graph elements, including marker symbols, lines, legends, captions, titles, axis labels, and grid lines. Reflecting the new graphics features of Stata, this thoroughly updated and expanded edition contains a new chapter that explains how to exploit the power of the new Graph Editor. This edition also includes additional examples and illustrates nearly every example with the Graph Editor.
Download or read book The SAGE Handbook of Regression Analysis and Causal Inference written by Henning Best and published by SAGE. This book was released on 2013-12-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
