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Applied Multivariate Statistical Analysis and Related Topics with R
  • 书号:9787030412430
    作者:吴浪,邱瑾
  • 外文书名:
  • 丛书名:浙江财经大学省级重点学科重点专业统计学系列教材
  • 装帧:平装
    开本:B5
  • 页数:226
    字数:
    语种:en
  • 出版社:科学出版社
    出版时间:1900-01-01
  • 所属分类:O 数理科学和化学 O69 应用化学 0714 统计学
  • 定价: ¥59.00元
    售价: ¥47.20元
  • 图书介质:
    纸质书 电子书

  • 购买数量: 件  可供
  • 商品总价:

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目录

  • Contents
    Preface
    Chapter 1 Introduction 1
    1.1 Goal of Statistics 1
    1.2 Univariate Analysis 3
    1.3 Multivariate Analysis 7
    1.4 Multivariate Normal Distribution 16
    1.5 Unsupervised Learning and Supervised Learning 21
    1.6 Data Analysis Strategies and Statistical Thinking 23
    1.7 Outline 26
    Exercises 1 27
    Chapter 2 Principal Components Analysis 29
    2.1 The Basic Idea 29
    2.2 The Principal Components 30
    2.3 Choose Number of Principal Components 34
    2.4 Considerations in Data Analysis 35
    2.5 Examples in R 37
    Exercises 2 43
    Chapter 3 Factor Analysis 45
    3.1 The Basic Idea 45
    3.2 The Factor Analysis Model 46
    3.3 Methods for Estimation 47
    3.4 Examples in R 50
    Exercises 3 54
    Chapter 4 Discriminant Analysis and Cluster Analysis 56
    4.1 Introduction 56
    4.2 Discriminant Analysis 57
    4.3 Cluster Analysis 61
    4.4 Examples in R 64
    Exercises 4 69
    Chapter 5 Inference for a Multivariate Normal Population 71
    5.1 Introduction 71
    5.2 Inference for Multivariate Means 72
    5.3 Inference for Covariance Matrices 75
    5.4 Large Sample Inferences about a Population Mean Vector 76
    5.5 Examples in R 76
    Exercises 5 79
    Chapter 6 Discrete or Categorical Multivariate Data 80
    6.1 Discrete or Categorical Data 80
    6.2 The Multinomial Distribution 81
    6.3 Contingency Tables 83
    6.4 Associations Between Discrete or Categorical Variables 85
    6.5 Logit Models for Multinomial Variables 87
    6.6 Loglinear Models for Contingency Tables 89
    6.7 Example in R 91
    Exercises 6 95
    Chapter 7 Copula Models 97
    7.1 Introduction 97
    7.2 Copula Models 99
    7.3 Measures of Dependence 102
    7.4 Applications in Actuary and Finance 103
    7.5 Applications in Longitudinal and Survival Data? 106
    7.6 Example in R 107
    Exercises 7 110
    Chapter 8 Linear and Nonlinear Regression Models 111
    8.1 Introduction 111
    8.2 Linear Regression Models 112
    8.3 Model Selection 114
    8.4 Model Diagnostics 116
    8.5 Data Analysis Examples with R 117
    8.6 Nonlinear Regression Models 122
    8.7 More on Model Selection 125
    Exercises 8 129
    Chapter 9 Generalized Linear Models 131
    9.1 Introduction 131
    9.2 The Exponential Family 132
    9.3 The General Form of a GLM 133
    9.4 Inference for GLM 135
    9.5 Model Selection and Model Diagnostics 137
    9.6 Logistic Regression Models 140
    9.7 Poisson Regression Models 146
    Exercises 9 149
    Chapter 10 Multivariate Regression and MANOVA Models 152
    10.1 Introduction 152
    10.2 Multivariate Regression Models 153
    10.3 MANOVA Models 156
    10.4 Examples in R 157
    Exercises 10 162
    Chapter 11 Longitudinal Data, Panel Data, and Repeated
    Measurements 164
    11.1 Introduction 164
    11.2 Methods for Longitudinal Data Analysis 165
    11.3 Linear Mixed Effects Models 167
    11.4 GEE Models 171
    Exercises 11 174
    Chapter 12 Methods for Missing Data 175
    12.1 Missing Data Mechanisms 175
    12.2 Methods for Missing Data 178
    12.3 Multiple Imputation Methods 181
    12.4 Multiple Imputation by Chained Equations 183
    12.5 The EM Algorithm 184
    12.6 Example in R 187
    Exercises 12 192
    Chapter 13 Robust Multivariate Analysis 193
    13.1 The Need for Robust Methods 193
    13.2 General Robust Methods 195
    13.3 Robust Estimates of the Mean and Standard Deviation 199
    13.4 Robust Estimates of the Covariance Matrix 201
    13.5 Robust PCA and Regressions 203
    13.6 Examples in R 205
    Exercises 13 210
    Chapter 14 Selected Topics 211
    14.1 Likelihood Methods 211
    14.2 Bootstrap Methods 214
    14.3 MCMC Methods and the Gibbs Sampler 215
    14.4 Survival Analysis 217
    14.5 Data Science, Big Data, and Data Mining 220
    References 224
    Index 225
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