Multivariate Statistics: by Härdle, Wolfgang Karl; Hlávka, Zdenek;

Long description:

There can be no question, my dear Watson, of the value of exercise before breakfast. Sherlock Holmes in “The Adventure of Black Peter” The statistical analysis of multivariate data requires a variety of techniques thatareentirelydi?erentfromtheanalysisofone-dimensionaldata.Thestudy of the joint distribution of many variables in high dimensions involves matrix techniques that are not part of standard curricula. The same is true for tra- formations and computer-intensive techniques, such as projection pursuit. The purpose of this book is to provide a set of exercises and solutions to help the student become familiar with the techniques necessary to analyze high-dimensional data. It is our belief that learning to apply multivariate statistics is like studying the elements of a criminological case. To become pro?cient, students must not simply follow a standardized procedure, they must compose with creativity the parts of the puzzle in order to see the big picture. We therefore refer to Sherlock Holmes and Dr. Watson citations as typical descriptors of the analysis. Puerile as such an exercise may seem, it sharpens the faculties of observation, and teaches one where to look and what to look for.

An ideal text for undergraduates in disciplines related to economics, this book could also help a university lecturer set up a modern multivariate analysis course. The authors have cleverly used exercises and their solutions to explore the concepts of multivariate data analysis. Broken down into three sections, this book has been structured to allow students in economics and finance to work their way through a well formulated exploration of this core topic. The book demonstrates the application of basic calculus and multivariate methods in real life situations. It contains more than 200 solved exercises, all of which are available in the R or XploRe languages. The corresponding libraries are downloadable from the Springer link web pages and from the author’s home pages.

Table of Contents:

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.