• 
  Applied ...
  ￥46.61元

无

样章试读

• 暂时还没有任何用户评论

总计 0 个记录，共 1 页。 第一页 上一页 下一页 最末页

全部咨询(共0条问答)

• 暂时还没有任何用户咨询内容

总计 0 个记录，共 1 页。 第一页 上一页 下一页 最末页

用户名：	匿名用户
E-mail：
咨询内容：

• 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