12.1 聚类分析的基本原理 12.1.1 什么是聚类分析? 12.1.2 相似性的度量 12.2 层次聚类 12.2.1 层次聚类的两种方式 12.2.2 类间距离的计算方法 12.2.3 层次聚类的应用 12.3 K-均值聚类 12.3.1 K-均值聚类的基本过程 12.3.2 K-均值聚类的应用 12.3.3 使用聚类方法的注意事项

一、目的要求 1.掌握多重线性回归的意义及用途； 2.掌握多重线性回归与相关的有关指标的含义。 3.熟悉多重相关与回归分析的基本原理和方法。 4.了解多重共线性的概念及其对回归分析结果的影响

一、目的要求 1. 掌握两个定量变量之间相关分析的意义、用途与假设检验。 2.掌握分类计数资料的两变量间关联性的定量分析方法

1 Numerical optimization methods in R 1.1 Root-finding in one dimension 1.1.1 Bisection method 1.1.2 Brent’s method 1.1.3 Newton’s method 1.1.4 Fisher scoring 1.2 multivariate optimization 1.2.1 Newton’s method and Fisher scoring 1.3 Numerical Integration 1.4 Maximum Likelihood Problems 1.5 Optimization Problems 1.5.1 One-dimension Optimization 1.5.2 multi-dimensional Optimization 1.6 Linear Programming

1 EM optimization method 1.1 EM algorithm 1.2 Convergence 1.3 Usage in exponential families 1.4 Usage in finite normal mixtures 1.5 Variance estimation 1.5.1 Louis method 1.5.2 SEM algorithm 1.5.3 Bootstrap method 1.5.4 Empirical Information 1.6 EM Variants 1.6.1 Improving the E step 1.6.2 Improving the M step 1.7 Pros and Cons

1 Markov Chain Monte Carlo Methods 1.4 The Gibbs Sampler 1.4.1 The Slice Gibbs Sampler 1.5 Monitoring Convergence 1.5.1 Convergence diagnostics plots 1.5.2 Monte Carlo Error 1.5.3 The Gelman-Rubin Method 1.6 WinBUGS Introduction 1.6.1 Building Bayesian models in WinBUGS 1.6.2 Model specification in WinBUGS 1.6.3 Data and initial value specification 1.6.4 Compiling model and simulating values

1 Markov Chain Monte Carlo Methods 1.1 Introduction 1.1.1 Integration problems in Bayesian inference 1.1.2 Markov Chain Monte Carlo Integration 1.1.3 Markov Chain 1.2 The Metropolis-Hastings Algorithm 1.2.1 Metropolis-Hastings Sampler 1.2.2 The Metropolis Sampler 1.2.3 Random Walk Metropolis 1.2.4 The Independence Sampler 1.3 Single-component Metropolis Hastings Algorithms 1.4 Application: Logistic regression

1 Bootstrap and Jackknife 1.1 The Bootstrap 1.1.1 Bootstrap Estimation of Standard Error 1.1.2 Bootstrap Estimation of Bias 1.2 Jackknife 1.3 Jackknife-after-Bootstrap 1.4 Bootstrap Confidence Intervals 1.4.1 The Standard Normal Bootstrap Confidence Interval 1.4.2 The Percentile Bootstrap Confidence Interval 1.4.3 The Basic Bootstrap Confidence Interval 1.4.4 The Bootstrap t interval 1.5 Better Bootstrap Confidence Intervals 1.6 Application: Cross Validation

1 Monte Carlo Methods in Inference 1.1 Monte Carlo Methods for Estimation 1.1.1 Monte Carlo Estimation and Standard Error 1.1.2 Estimation of MSE 1.2 Estimating a confidence level 1.3 Monte Carlo Methods for Hypothesis Tests 1.4 Empirical Type I error rate 1.4.1 Power of a Test 1.4.2 Power Comparisons 1.5 Application: “Count Five” Test for Equal Variance

1 Methods for Generating Random Variables 1.1 Generating Uniform(0,1) random number 1.2 Random Generators of Common Probability Distribution in R 1.2.1 The Inverse Transform Method 1.2.2 The Acceptance-Rejection Method 1.2.3 Transformation Methods 1.2.4 Sums and Mixtures 1.3 Multivariate Distribution 1.3.1 Multivariate Normal Distribution 1.3.2 Mixtures of Multivariate Normals 1.3.3 Wishart Distribution 1.3.4 Uniform Distribution on the d−Sphere 1.4 Stochastic Process