17
resampling
R Note for Statistics Learning and Computing
Preface
1
R Basics
2
R Programming for Data Analysis
3
Datasets to Practice
4
Exploratory Data Analysis
5
Hypothesis Test
6
Linear Regression
7
ANalysis of VAriance (ANOVA)
8
Logistics Regression
9
Generalized Linear Regression
10
Classification Models
11
Neural Network
12
Decision Tree and Random Forests
13
Cluster
14
Dimensionality Reduction
15
Generate Random Variables
16
Monte Carlo
17
resampling
18
Expection - Maximized
References
Table of contents
17.1
Bootstrapping
17.2
Jackknife
17.3
Cross validation
17
resampling
Resampling (statistics)
17.1
Bootstrapping
17.2
Jackknife
Jackknife resampling
17.3
Cross validation
16
Monte Carlo
18
Expection - Maximized