• Descriptive Techniques.- Comparison of Batches.- Multivariate Random Variables.- A Short Excursion into Matrix Algebra.- Moving to Higher Dimensions.- Multivariate Distributions.- Theory of the Multinormal.- Theory of Estimation.- Hypothesis Testing.- Multivariate Techniques.- Decomposition of Data Matrices by Factors.- Principal Component Analysis.- Factor Analysis.- Cluster Analysis.- Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Applications in Finance.- Highly Interactive, Computationally Intensive Techniques.

"In general, I find this book particularly instructive, by discussing various techniques and analytical tools via exercises with rigorous solutions. The computer codes for computer-based exercises are available in R or XploRe languages through the Springer link web pages and from the authors' home pages. The web links also provide access to real datasets used in the book. This is a very useful exercise book for students and instructors as well as for nonexperts using in applied multivariate data analysis. There has been large demand for techniques to handle and analyze high-dimensional data. In this regard, the book would be a good reference for researchers and students working in the theory or applications of multivariate statistical analysis." (Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)

• Descriptive Techniques.- Comparison of Batches.- Multivariate Random Variables.- A Short Excursion into Matrix Algebra.- Moving to Higher Dimensions.- Multivariate Distributions.- Theory of the Multinormal.- Theory of Estimation.- Hypothesis Testing.- Multivariate Techniques.- Decomposition of Data Matrices by Factors.- Principal Component Analysis.- Factor Analysis.- Cluster Analysis.- Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Applications in Finance.- Highly Interactive, Computationally Intensive Techniques.

"In general, I find this book particularly instructive, by discussing various techniques and analytical tools via exercises with rigorous solutions. The computer codes for computer-based exercises are available in R or XploRe languages through the Springer link web pages and from the authors' home pages. The web links also provide access to real datasets used in the book. This is a very useful exercise book for students and instructors as well as for nonexperts using in applied multivariate data analysis. There has been large demand for techniques to handle and analyze high-dimensional data. In this regard, the book would be a good reference for researchers and students working in the theory or applications of multivariate statistical analysis." (Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)

• Descriptive Techniques.- Comparison of Batches.- Multivariate Random Variables.- A Short Excursion into Matrix Algebra.- Moving to Higher Dimensions.- Multivariate Distributions.- Theory of the Multinormal.- Theory of Estimation.- Hypothesis Testing.- Multivariate Techniques.- Decomposition of Data Matrices by Factors.- Principal Component Analysis.- Factor Analysis.- Cluster Analysis.- Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Applications in Finance.- Highly Interactive, Computationally Intensive Techniques.

"In general, I find this book particularly instructive, by discussing various techniques and analytical tools via exercises with rigorous solutions. The computer codes for computer-based exercises are available in R or XploRe languages through the Springer link web pages and from the authors' home pages. The web links also provide access to real datasets used in the book. This is a very useful exercise book for students and instructors as well as for nonexperts using in applied multivariate data analysis. There has been large demand for techniques to handle and analyze high-dimensional data. In this regard, the book would be a good reference for researchers and students working in the theory or applications of multivariate statistical analysis." (Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)