Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization

ISBN-13: 9780387952963 / Angielski / Twarda / 2001 / 398 str.

Ronald Christensen

Opis

This is the second edition of Linear Models for Multivariate, Time Series and Spatial Data. It has a new title to indicate that it contains much new material. The primary changes are the addition of two new chapters: one on nonparametric regression and one on response surface maximization. As before, the presentations focus on the linear model aspects of the subject. For example, in the nonparametric regression chapter there is very little about kernal regression estimation but quite a bit about series approxi mations, splines, and regression trees, all of which can be viewed as linear modeling. The new edition also includes various smaller changes. Of particular note are a subsection in Chapter 1 on modeling longitudinal (repeated measures) data and a section in Chapter 6 on covariance structures for spatial lattice data. I would like to thank Dale Zimmerman for the suggestion of incor porating material on spatial lattices. Another change is that the subject index is now entirely alphabetical."

Recenzje

From the reviews of the second edition:

"The book by Christensen provides a good explanation of the theory of linear models, spatial data analysis, kriging, nonparametric regressions, response surface maximization, longitudinal models, discriminant analysis, principal components, factor analysis, frequency and time domain time series among others. This is an excellent book and I enjoyed reading every chapter. The author is known for clear presentation. Statistical software MINITAB, BMDP, and MSUSTAT are used in the data analysis of the book." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 75 (7), 2005)

"The book gives an introduction to a diverse collection of methodologies using the framework of linear models. Many examples are given, and all chapters are finished with a bunch of additional exercises. ... The contents are very well explained and really illuminating for people familiar with linear models. I consider this book to be a didactically excellent textbook for graduate students who want to get an overview on advanced linear statistical methods." (R. Fried, Metrika, September, 2003)

"The author aims at a unified approach for different themes, systematically exploiting 'three fundamental ideas from standard linear model theory: best linear prediction, projections, and Mahalanobis distances'. ... It is seen that this book gives a thorough coverage of a broad area, with emphasis on its mathematical aspects, which are clearly presented. It may be useful to those having already some acquaintance with the themes treated, and wishing to study them from another point of view." (Ricardo Maronna, Statistical Papers, Vol. 44 (4), 2003)

"Advanced Linear Modeling is a retitled second edition of Linear Models for Multivariate, Time Series, and Spatial Data (Christensen 1991). ... I find it enlightening and a pleasure to read. Christensen writes in an engaging, informal style that carries the reader along through some challenging material. His linear models framework offers many new insights into the topics that he covers. I expect that I will refer to the book repeatedly when I have occasion to study these topics further." (Daniel B. Hall, Journal of the American Statistical Association, March, 2003)

"This book is the second edition of Linear Models for Multivariate, Time Series and Spatial Data (1991) ... . The main change is the addition of Chapter 7 on nonparametric regression ... and Chapter 8 on response surface maximization. The emphasis in this work is on the linear model theory, which unifies three major fields in statistics: multivariate analysis, time series and spatial data. Most chapters end with a selection of exercises, which makes the book also interesting for teaching purposes." (N. D. C. Veraverbeke, Short Book Reviews, Vol. 22 (1), 2002)

Spis treści

Multivariate Linear Models * Discrimination and Allocation * Principal Components and Factor Analysis * Frequency Analysis of Time Series * Time Domain Analysis * Linear Models for Spatial Data: Kriging * Nonparametric Regression * Response Surface Maximization