0.1 Computational_Statistics_with_R.pdf (8MB)
0.2 Syllabus
0.3 Assignments
0.4 Resma3.RData (Ver 3.2)
1.1 Installation and updating
1.2 R Markdown, HTML and Latex
1.3 R basics
1.4 Programming in R
1.5 Random numbers and simulation
1.6 Graphs with ggplot2
1.7 List of base R commands
2.1 Descriptive statistics
2.2 Parameter estimation
2.3 EM algorithm
2.4 Confidence intervals
2.5 Hypothesis testing
2.6 Bayesian statistics
2.7 Nuisance parameters
2.8 Simulation
2.9 The Bootstrap
2.10 Basic inferences
2.11 ANOVA
2.12 Transformations and nonparametric methods
2.13 Model checking
3.1 Simple regression
3.2 Assumptions
3.3 Prediction
3.4 Nonlinear models
3.5 Finding the best model
3.6 Nonparametric regression
3.7 Nonlinear parametric models
3.8 Logistic regression
4.1 ANOVA
4.2 Multiple regression
4.3 Models with dummy variables
4.4 Generalized additive models
4.5 Subset selection and ridge regression
4.6 Regression trees
4.7 Principal components analysis
5.1 Introduction
5.2 LDA, QDA and k nearest neighbor
5.3 Regression trees
5.4 Neural networks and support vector machines
5.5 Examples
5.6 Other Methods
5.7 Deep Learning
6.1 Survival analysis
6.2 Nonparametric density estimation
6.3 Time series analysis
6.4 Bayesian Analysis with OpenBugs