Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics)
| Author | : | |
| Rating | : | 4.35 (576 Votes) |
| Asin | : | 0387781889 |
| Format Type | : | paperback |
| Number of Pages | : | 733 Pages |
| Publish Date | : | 2017-04-07 |
| Language | : | English |
DESCRIPTION:
A great step forward in the way we look at multivariate data This book surprised me. I was expecting a book filled with a discussion of mostly traditional multivariate techniques supplemented by a few chapters of more recent developments. Instead, I found a completely new and refreshing approach to statistics and data exploration that framed the classical regression approach to mo. Tseng, Chien-han said nice reference. This book not only covers very wide ranges about ststistical learning but also has very deep discriptions in some topics. This is a good book especially for graduate students.. Nice material or PhD students Dmitry SHALYMOV Good observation of modern approaches for classification and clustering problems. Nice structure of material and nice paper =)
… persons interested in learning new trends of multivariate methods would find Izenman’s book very helpful. From the reviews:"This book will be enjoyed by those who wish to understand the current state of multivariate statistical analysis in an age of high-speed computation and large data sets. It is addressed to readers with a background in probability, statistical theory, multivariate calculus, linear algebra and notions of Bayesian methods. It seems to have full potential to become a second standard reference next to Hastie et al. More than 200 exercises are presented in the book." (J. …The first
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.