Inference in Hidden Markov Models

of the simple linear regression model. Multiple linear regression for two variables is discussed in Chapter 8, and that for more than two variables is covered in Chapter 9. Chapter 10, on model building, is perhaps the book’s strongest chapter. The authors provide one of the most intuitive discussions on variable transformations that I have seen. Nice presentations of indicator variables, variable selection, and influence diagnostics are also provided. The final chapter covers a wide variety of topics, including analysis of variance models, logistic regression, and robust regression. The coverage of regression is not matrix-based, but optional linear algebra sections at the end of each chapter are useful for one wishing to use matrices. In general, the writing is clear and conceptual. A good number of exercises (about 20 on average) at the end of each chapter are provided. The exercises emphasize derivations and computations. It is difficult to name some comparison texts. Certainly, the text by Ott and Longnecker (2001) would be more suitable for a statistical methods course for an interdisciplinary audience. The regression texts of Montgomery, Peck, and Vining (2001) and Mendenhall and Sincich (2003) are more comprehensive in the regression treatment than the reviewed text. However, Introduction to Linear Models and Statistical Inference is not meant to compete with these texts—rather, its audience is primarily those taking a statistics course within a mathematics department.