Abstract:Most inferential procedures in the book are based on the multivariate normal distribution. The properties of multivariate normal random variables include the following: linear functions are normal, ce...Most inferential procedures in the book are based on the multivariate normal distribution. The properties of multivariate normal random variables include the following: linear functions are normal, certain quadratic functions have a chi-square distribution, any subset of variables has a normal distribution, any two subvectors are independent if their covariances are all zero, the conditional distribution of a subvector adjusted for another is normal. Estimates of the mean vector and covariance matrix are given, along with the distribution of these estimators. There is a multivariate central limit theorem corresponding to the univariate central limit theorem. Methods are given for assessing both univariate and multivariate normality of a sample. Some of these methods involve test statistics, and tables of critical values are given in Appendix A. Methods for detecting outliers in either univariate or multivariate data are discussed. Numerical and graphical illustrations are given, and the problems at the end of the chapter call for derivation of some of the results in the chapter and provide additional numerical illustrations.Read More
Publication Year: 2002
Publication Date: 2002-02-22
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 1
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