Multivariate statistics : high-dimensional and large-sample approximations /
A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of...
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| Main Author: | |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, N.J. :
Wiley,
©2010.
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| Subjects: | |
| Online Access: | CONNECT |
Table of Contents:
- Front Matter
- Multivariate Normal and Related Distributions
- Wishart Distribution
- Hotelling's T and Lambda Statistics
- Correlation Coefficients
- Asymptotic Expansions for Multivariate Basic Statistics
- MANOVA Models
- Multivariate Regression
- Classical and High-Dimensional Tests for Covariance Matrices
- Discriminant Analysis
- Principal Component Analysis
- Canonical Correlation Analysis
- Growth Curve Analysis
- Approximation to the Scale-Mixted Distributions
- Approximation to Some Related Distributions
- Error Bounds for Approximations of Multivariate Tests
- Error Bounds for Approximations to Some Other Statistics
- Appendix
- Bibliography
- Index
- Wiley Series in Probability and Statistics.
- Multivariate normal and related distributions
- Wishart distribution
- Hotelling's T² and lambda statistics
- Correlation coefficents
- Asymptotic expansions from multivariate basic statistics
- MANOVA models
- Multivariate regression
- Classical and high-dimensional tests for covariance matrices
- Discriminant analysis
- Principal component analysis
- Canonical correlation analysis
- Growth curve analysis
- Approximation to the scale-mixted distributions
- Approximation to some related distributions
- Error bounds for approximations of multivariate tests
- Error bounds for approximations to some other statistics
- Appendix.
