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0 (hodnocen0 x )

EB

ONLINE

1st ed.

Les Ulis : EDP Sciences, 2021

1 online resource (238 pages)

ISBN 9782759826025 (electronic bk.)

ISBN 9782759826018

Current Natural Sciences Ser.

Print version: WU, Lang Applied Multivariate Statistical Analysis and Related Topics with R Les Ulis : EDP Sciences,c2021 ISBN 9782759826018

Intro -- Applied Multivariate Statistical Analysis and Related Topics with R -- Preface -- Contents -- Chapter 1 Introduction -- 1.1 Goal of Statistics -- 1.2 Univariate Analysis -- 1.3 Multivariate Analysis -- 1.4 Multivariate Normal Distribution -- 1.5 Unsupervised Learning and Supervised Learning -- 1.6 Data Analysis Strategies and Statistical Thinking -- 1.7 Outline -- Exercises 1 -- Chapter 2 Principal Components Analysis -- 2.1 The Basic Idea -- 2.2 The Principal Components -- 2.3 Choose Number of Principal Components -- 2.4 Considerations in Data Analysis -- 2.5 Examples in R -- Exercises 2 -- Chapter 3 Factor Analysis -- 3.1 The Basic Idea -- 3.2 The Factor Analysis Model -- 3.3 Methods for Estimation -- 3.4 Examples in R -- Exercises 3 -- Chapter 4 Discriminant Analysis and Cluster Analysis -- 4.1 Introduction -- 4.2 Discriminant Analysis -- 4.3 Cluster Analysis -- 4.4 Examples in R -- Exercises 4 -- Chapter 5 Inference for a Multivariate Normal Population -- 5.1 Introduction -- 5.2 Inference for Multivariate Means -- 5.3 Inference for Covariance Matrices -- 5.4 Large Sample Inferences about a Population Mean Vector -- 5.5 Examples in R -- Exercises 5 -- Chapter 6 Discrete or Categorical Multivariate Data -- 6.1 Discrete or Categorical Data -- 6.2 The Multinomial Distribution -- 6.3 Contingency Tables -- 6.4 Associations Between Discrete or Categorical Variables -- 6.5 Logit Models for Multinomial Variables -- 6.6 Loglinear Models for Contingency Tables -- 6.7 Example in R -- Exercises 6 -- Chapter 7 Copula Models -- 7.1 Introduction -- 7.2 Copula Models -- 7.3 Measures of Dependence -- 7.4 Applications in Actuary and Finance -- 7.5 Applications in Longitudinal and Survival Data∗ -- 7.6 Example in R -- Exercises 7 -- Chapter 8 Linear and Nonlinear Regression Models -- 8.1 Introduction -- 8.2 Linear Regression Models.

8.3 Model Selection -- 8.4 Model Diagnostics -- 8.5 Data Analysis Examples with R -- 8.6 Nonlinear Regression Models -- 8.7 More on Model Selection -- Exercises 8 -- Chapter 9 Generalized Linear Models -- 9.1 Introduction -- 9.2 The Exponential Family -- 9.3 The General Form of a GLM -- 9.4 Inference for GLM -- 9.5 Model Selection and Model Diagnostics -- 9.6 Logistic Regression Models -- 9.7 Poisson Regression Models -- Exercises 9 -- Chapter 10 Multivariate Regression and MANOVA Models -- 10.1 Introduction -- 10.2 Multivariate Regression Models -- 10.3 MANOVA Models -- 10.4 Examples in R -- Exercises 10 -- Chapter 11 Longitudinal Data, Panel Data, and Repeated Measurements -- 11.1 Introduction -- 11.2 Methods for Longitudinal Data Analysis -- 11.3 Linear Mixed Effects Models -- 11.4 GEE Models -- Exercises 11 -- Chapter 12 Methods for Missing Data -- 12.1 Missing Data Mechanisms -- 12.2 Methods for Missing Data -- 12.3 Multiple Imputation Methods -- 12.4 Multiple Imputation by Chained Equations -- 12.5 The EM Algorithm -- 12.6 Example in R -- Exercises 12 -- Chapter 13 Robust Multivariate Analysis -- 13.1 The Need for Robust Methods -- 13.2 General Robust Methods -- 13.3 Robust Estimates of the Mean and Standard Deviation -- 13.4 Robust Estimates of the Covariance Matrix -- 13.5 Robust PCA and Regressions -- 13.6 Examples in R -- Exercises 13 -- Chapter 14 Selected Topics -- 14.1 Likelihood Methods -- 14.2 Bootstrap Methods -- 14.3 MCMC Methods and the Gibbs Sampler -- 14.4 Survival Analysis -- 14.5 Data Science, Big Data, and Data Mining -- Reference.

001904791

express

(Au-PeEL)EBL6522893

(MiAaPQ)EBC6522893

(OCoLC)1243546702