Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (Chapman & Hall/CRC Texts in Statistical Science)

James S. Hodges

ISBN 10: 0367533731 / ISBN 13: 9780367533731

Published by Routledge 2021-06-30, 2021

About this Item

Seller Inventory # 6666-IUK-9780367533731

Bibliographic Details

• Title: Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects (Chapman & Hall/CRC Texts in Statistical Science)
• Publisher: Routledge 2021-06-30
• Publication Date: 2021
• Language: English
• ISBN 10: 0367533731
• ISBN 13: 9780367533731
• Binding: Paperback
• Condition: New

About this title

Synopsis

A First Step toward a Unified Theory of Richly Parameterized Linear Models

Using mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects.

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The author examines what is known and unknown about mixed linear models and identifies research opportunities.

The first two parts of the book cover an existing syntax for unifying models with random effects. The text explains how richly parameterized models can be expressed as mixed linear models and analyzed using conventional and Bayesian methods.

In the last two parts, the author discusses oddities that can arise when analyzing data using these models. He presents ways to detect problems and, when possible, shows how to mitigate or avoid them. The book adapts ideas from linear model theory and then goes beyond that theory by examining the information in the data about the mixed linear model’s covariance matrices.

Each chapter ends with two sets of exercises. Conventional problems encourage readers to practice with the algebraic methods and open questions motivate readers to research further. Supporting materials, including datasets for most of the examples analyzed, are available on the author’s website.

About the Author

James S. Hodges