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Contents
Front Matter
Introduction
1.
Introduction to Longitudinal Data Analysis
2.
Exploratory Data Analysis forLongitudinal Data
3.
General Linear Model forLongitudinal Data Analysis-1
4.
General Linear Model forLongitudinal Data Analysis-2
5.
General Linear Model forLongitudinal Data Analysis-3
6.
General Linear Model forLongitudinal Data Analysis-4
7.
Linear Mixed Model for LongitudinalData Analysis-1
8.
Linear Mixed Model for LongitudinalData Analysis-2
9.
Linear Mixed Model for LongitudinalData Analysis-3
10.
Marginal Models for LongitudinalData Analysis-1
11.
Marginal Models for LongitudinalData Analysis-2
12.
Transition Models for LongitudinalData Analysis
13.
Review of Longitudinal DataAnalysis-2
14.
Review of Longitudinal DataAnalysis-1
15.
Likelihood for Missing DataAnalysis-1
16.
Likelihood for Missing DataAnalysis-1
17.
Likelihood for Missing DataAnalysis-2
18.
he Expectation Maximisation (EM)Algorithm
19.
The Expectation Maximisation (EM)Algorithm in R
20.
Some properties of EM algorithm
21.
Missing data analysis- AnApplication of EM algorithm in R
22.
Missing data analysis- MultipleImputation
23.
Missing data analysis- MultipleImputation in R
24.
Summary of missing data analysis
25.
Motivation for Bootstrap
26.
The Jackknife
27.
The Bootstrap
28.
he Bootstrap: Some examples
29.
The Bootstrap: Condence Intervals1
30.
The Bootstrap: Computationalmethods
31.
Some Applications in Bootstrap-1
32.
Some Applications in Bootstrap-2
33.
Bootstrap condence intervals in R
34.
The Bootstrap: Computational mathods
35.
Bootstrap linear model
36.
Bootstrap resistant regression: Anapplication in R
37.
The Boot package
38.
Permutation tests
39.
Cross Validation
40.
The Bootstrap: Summary
Back Matter
Appendix
Advanced Data Analysis
39
Cross Validation
References
http://robjhyndman.com/hyndsight/crossvalidation/
Arlot S and Celisse A (2010): A survey of cross-validation procedures for model selection. Statistics Surveys, Vol 4, pp 40-79
