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Applied Multivariate Statistical Analysis and Related Topics with R

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Multivariate analysis is a popular area in statistics and data science. This book provides a good balance between conceptual understanding, key theoretical presentation, and detailed implementation with software R for commonly used multivariate analysis models and methods in practice.

Author(s): Lang WU; Jin QIU
Publisher: EDP Sciences
Year: 2021

Language: English
Pages: 223
Tags: Multivariate Statistics, PCA, Factor Analysis, Discriminamt Analysis, Clusyer Analysis, Copula Models, GLM, R

Contents
Chapter 1 Introduction
Chapter 2 Principal Components Analysis
Chapter 3 Factor Analysis
Chapter 4 Discriminant Analysis and Cluster Analysis
Chapter 5 Inference for a Multivariate Normal Population
Chapter 6 Discrete or Categorical Multivariate Data
Chapter 7 Copula Models
Chapter 8 Linear and Nonlinear Regression Models
Chapter 9 Generalized Linear Models
Chapter 10 Multivariate Regression and MANOVA Models
Chapter 11 Longitudinal Data, Panel Data, and Repeated Measurements
Chapter 12 Methods for Missing Data
Chapter 13 Robust Multivariate Analysis
Chapter 14 Selected Topics
References