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A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling
We do not plan to review this book.
Wide Range of Applications
The Resampling Methods
Fields of Application
Estimation and the Bootstrap
Precision of an Estimate
Confidence Intervals
Improved Confidence Intervals
Estimating Bias
Determining Sample Size
Software for Use with the Bootstrap and Permutation Tests
AFNI
Blossom Statistical Analysis Package
Eviews
HaploView
MatLab®
NCSS
PAUP
R.
SAS
S-Plus
SPSS Exact Tests
Stata
Statistical Calculator
StatXact
Testimate
Comparing Two Populations
A Distribution-Free Test
Some Statistical Considerations
Computing the p-Value
Other Two-Sample Comparisons
Two-Sided Test
Rank Tests
Matched Pairs
R Code
Stata
Test for Nonequivalence
Underlying Assumptions
Comparing Variances
Multiple Variables
Single-Valued Test Statistic
Combining Univariate Tests
Experimental Design and Analysis
Separating Signal from Noise
k-Sample Comparison
Multiple Factors
Eliminating the Effects of Multiple Covariates
Crossover Designs
Which Sets of Labels Should We Rearrange?
Determining Sample Size
Missing Combinations
Categorical Data
Fisher’s Exact Test.
Odds Ratio.4
Unordered r × c Contingency Tables
Ordered Statistical Tables
Multidimensional Arrays
Multiple Hypotheses
Controlling the Family-Wise Error Rate
Controlling the False Discovery Rate
Software for Performing Multiple Simultaneous Tests
Testing for Trend
Model Building
Regression Models
Applying the Permutation Test
Applying the Bootstrap
Prediction Error
Validation
Classification
Cluster Analysis
Classification
Decision Trees
Decision Trees vs. Regression
Which Decision Tree Algorithm Is Best for Your Application?
Reducing the Rate of Misclassification
Comparison of Classification Tree Algorithms
Validation vs. Cross-Validation
Restricted Permutations
Quasi Independence
Complete Factorials
Synchronized Permutations
Model Validation
References
Appendix A: Basic Concepts in Statistics
Additive vs. Multiplicative Models
Central Values
Combinations and Rearrangements
Dispersion
Frequency Distribution and Percentiles
Linear vs. Nonlinear Regression
Regression Methods
Appendix B: Proof of Theorems
Dummy View - NOT TO BE DELETED