Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics)
| Author | : | |
| Rating | : | 4.66 (617 Votes) |
| Asin | : | 0387781889 |
| Format Type | : | paperback |
| Number of Pages | : | 733 Pages |
| Publish Date | : | 2016-08-20 |
| Language | : | English |
DESCRIPTION:
…This book would be a fantastic reference for researchers interested in learning about multivariate and machine learning methods. 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. More than 60 data sets are used for working out as examples. … persons interested in learning new trends of multivariate methods would find Izenman’s book very helpful. It seems to have full potential to become a second standard reference next to Hastie et al. It is addressed to readers with a background in probability, statistical theory, multivariate calculu
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.
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 class. 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 Good observation of modern approaches for classification and clustering problems. Nice structure of material and nice paper =)