Multivariate Distributions, Inference, Regression and Canonical Correlation
Part of the
Springer Texts in Statistics
book series (STS)
Before we introduce additional techniques for multivariate analysis, it is necessary to explain notation for multivariate random variables and samples. Since many multivariate inference procedures require a multivariate normal distribution assumption, an introduction to this distribution is also provided here. In addition, the chapter includes an outline of inference procedures for the mean vector and covariance matrix. In some applications multivariate random variables are partitioned into two or more subsets. The relationship between the variables in different sets is often of interest. In the last section of this chapter we outline the techniques of multivariate regression and canonical correlation in order to study the relationships between subsets of random variables.
KeywordsCovariance Matrix Maximum Likelihood Estimator Mahalanobis Distance Canonical Correlation Canonical Correlation Analysis
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