Probability and Mathematical Statistics
This long-awaited revision combines a theoretical and data analytic approach to the subject, whilst emphasising modern developments in the field. Topics covered include econometrics, principal component analysis, factor analysis, canonical correlation analysis, discriminate analysis, cluster analysis, multi-dimensional scaling and directional data. Several methods of presentation, which helped make the first edition popular with professional statisticians and students alike, are used. For example, the data matrix is emphasised throughout, and density-free approach is given to normal theory. Tests are constructed using the likelihood ratio principle and the union intersection principle, and graphical methods are used in explanation.
Researchers in probability and statistics.