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Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book …
"An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling." "This edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It cover…
"This Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multi…
This book provides a practical guide for the use of modern statistical methods within agricultural and natural resources sciences. Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences is being released world-wide at an important time of …
Linear Algebra and Linear Models comprises a concise and rigorous introduction to linear algebra required for statistics followed by the basic aspects of the theory of linear estimation and hypothesis testing. The emphasis is on the approach using generalized inverses. Topics su…
"This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets…
Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generaliza…
