Multivariate Statistics: - Wolfgang Karl Härdle

. .

Résumé :
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....

Biographie:
Wolfgang Karl H?rdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universit?t zu Berlin and director of C.A.S.E. (Center for Applied Statistics and Economics), director of the CRC-649 (Collaborative Research Center) Economic Risk and director of the IRTG 1792 High Dimensional Non-stationary Time Series. He teaches quantitative finance and semi-parametric statistics.? His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to the Guanghua School of Management, Peking University, and senior fellow of Sim Kee Boon Institute of Financial Economics at the Singapore Management University. Zdenek Hl?vka studied mathematics at the Charles University in Prague and biostatistics at Limburgs Universitair Centrum in Diepenbeek. Later he held a position at Humboldt-Universit?t zu Berlin before he became amember of the Department of Probability and Mathematical Statistics at Charles University in Prague....

Sommaire:
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....