Methods of Multivariate Statistics
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In this volume, Srivastava examines both random variables that can be quantitatively measured as well as the latest multivariate methods. Most of the methods presented assume that the data has a normal distribution and that there are no outliers. Also, a majority of the test statistics and estimates included in the book are based on the maximum likelihood method.
Get up-to-speed on the latest methods of multivariate statistics
Multivariate statistical methods provide a powerful tool for analyzing data when observations are taken over a period of time on the same subject. With the advent of fast and efficient computers and the availability of computer packages such as S-plus and SAS, multivariate methods once too complex to tackle are now within reach of most researchers and data analysts. With an emphasis on computing techniques in combination with a full understanding of the mathematics behind the methods, Methods of Multivariate Statistics offers an up-to-date account of multivariate methods. Focusing on the maximum likelihood method for estimation, testing of hypotheses, and "profile analysis," this book offers comprehensive discussions of commonly encountered multivariate data and also covers some practical and important problems lacking in other texts. These include:
_ Missing at-random observations
_ "Growth Curve Models" and multivariate one-sided tests applicable in pharmaceutical and medical trials
_ Bootstrap methods
_ Principal component method for predicting a multivariate response vector
_ Outlier detection and handling inference when covariance is singular
With clear chapter introductions and numerous problem sets, Methods of Multivariate Statistics meets every statistician's need for a comprehensive investigation of the latest methods in multivariate statistics.
Multivariate statistical methods provide a powerful tool for analyzing data when observations are taken over a period of time on the same subject. With the advent of fast and efficient computers and the availability of computer packages such as S-plus and SAS, multivariate methods once too complex to tackle are now within reach of most researchers and data analysts. With an emphasis on computing techniques in combination with a full understanding of the mathematics behind the methods, Methods of Multivariate Statistics offers an up-to-date account of multivariate methods. Focusing on the maximum likelihood method for estimation, testing of hypotheses, and "profile analysis," this book offers comprehensive discussions of commonly encountered multivariate data and also covers some practical and important problems lacking in other texts. These include:
_ Missing at-random observations
_ "Growth Curve Models" and multivariate one-sided tests applicable in pharmaceutical and medical trials
_ Bootstrap methods
_ Principal component method for predicting a multivariate response vector
_ Outlier detection and handling inference when covariance is singular
With clear chapter introductions and numerous problem sets, Methods of Multivariate Statistics meets every statistician's need for a comprehensive investigation of the latest methods in multivariate statistics.
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