Jeffrey S. Simonoff Libros

Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.

Categorical data frequently appear across various fields such as biometrics, economics, management, and sociology. This book introduces the analysis of such data, primarily utilizing loglinear Poisson regression and logistic binomial regression models. It covers a wide array of topics, including count regression models like Poisson, negative binomial, and zero-inflated models; loglinear models for contingency tables; and regression models for binary and multiple-category target variables. The methods are illustrated with real data analyses from recent journal articles, providing detailed context, model checking, and scientific implications. Over 200 exercises are included, many based on current literature, with data sets and computer code available online. The text has received positive feedback for its practical approach and extensive examples, making it suitable for readers with varying statistical backgrounds. It emphasizes the connection between statistical methods and regression modeling, offering insights into both common and less frequently covered topics. Overall, the book serves as a comprehensive resource for researchers and practitioners dealing with categorical data, providing clear methodologies and practical guidelines for analysis.

This comprehensive handbook serves as a vital resource for students and practitioners engaged in statistical regression analyses using R. It offers practical guidance and reference material, covering essential techniques and methodologies. The book is designed to enhance understanding and application of regression models, making complex concepts accessible. With a focus on real-world applications, it equips readers with the tools necessary to effectively conduct analyses and interpret results in various contexts.