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Mathematics - Probability Theory and Stochastic Processes | Linear Models for Multivariate, Time Series, and Spatial Data

Linear Models for Multivariate, Time Series, and Spatial Data

Christensen, Ronald

1991, XII, 318 p.

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This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem­ ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail­ able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understanding the world.

Content Level » Graduate

Keywords » Covariance matrix - Factor analysis - Logistic Regression - Observable - Time series - Variance - analysis of variance - data analysis

Related subjects » Applications - Probability Theory and Stochastic Processes

Table of contents 

I Multivariate Linear Models.- II Discrimination and Allocation.- III Principal Components and Factor Analysis.- IV Frequency Analysis of Time Series.- V Time Domain Analysis.- VI Linear Models for Spatial Data: Kriging.- References.- Author Index.

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