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[此贴子已经被作者于2005-1-17 2:45:28编辑过]

by Julian L. Simon Second Edition published October 1997

This text grew out of chapters in the 1969 edition of Basic Research Methods in Social Science by the same author, and contains the first published example of what was later called the bootstrap. Simon is best known for his research in demography, population and the economics of natural resources, and gained fame when the noted biologist Paul Ehrlich selected five commodities and bet Simon that scarcity would drive their prices up over the period of the bet (in fact, their prices all dropped). Resampling: The New Statistics contains a number of examples in Resampling Stats, a computer program originated by Simon, but can be read on its own without the program.

Table of Contents

Preface-A Look Back and A Look Ahead Introduction-Uses Of Probability and Statistics Afternote 1 Afternote 2 Chap 1-The Resampling Method of Solving Problems Chap 2-Basic Concepts in Probability and Statistics Chap 3-Basic Concepts in Probability and Statistics Chap 4-Probability Theory Chap 5-Probability Theory continued Chap 6-Probability Theory Part 2, Compound Probability Chap 7-Probability Theory, Part 3 Chap 8-Probability Theory, Part 4, Estimating Probabilities from Finite Universes: Chap 9-On Variability in Sampling Chap 10-The Procedures of Monte Carlo Simulation (and Resampling) Chap 11-The Basic Ideas in Statistical Inference Chap 12-Introduction to Statistical Inference Chap 13-Point Estimation Chap 14-Framing Statistical Questions Chap 15-Hypothesis-Testing with Counted Data, Part 1 Chap 16-The Concept of Statistical Significance in Testing Hypotheses Chap 17-The Statistics of Hypothesis-Testing With Counted Data, Part 2 Chap 18-The Statistics of Hypothesis-Testing With Measured Data Chap 19-General Procedures for Testing Hypotheses Chap 20-Confidence Intervals, Part 1, Assessing the Accuracy of Samples Chap 21-Confidence Intervals, Part 2, The Two Approaches to Estimating Confidence Intervals Chap 22-And Some Last Words About the Reliability of Sample Averages Chap 23-Correlation and Causation Chap 24-How Big a Sample Chap 25-Bayesian Analysis by Simulation Exercise Solutions Acknowledgements References Tech Note

[此贴子已经被作者于2005-1-17 3:40:55编辑过]

Resampling Methods: A Practical Guide to Data Analysis, 2nd ed

Title: Resampling Methods: A Practical Guide to Data Analysis 2nd edition Author: Phillip I. Good Publisher: Birkhäuser Copyright: 2001 ISBN: 0-8176-4243-9 Pages: 238; hardcover

Table of contents

Preface

1 Descriptive Statistics

1.1 Statistics

1.2 Reporting Your Results

1.3 Picturing Data

1.3.1 Graphs

1.3.2 From Observations to Questions

1.3.3 Contingency Tables

1.3.4 Types of Data

1.4 Measures of Location

1.4.1 Arithmetic Mean

1.4.2 Geometric Mean

1.5 Measures of Dispersion

1.6 Sample Versus Population

1.6.1 Statistics and Parameters

1.6.2 Estimating Population Parameters

1.6.3 Precision of an Estimate

1.6.4 Caveats

1.7 Summary

1.8 To Learn More

1.9 Exercises

2 Testing a Hypothesis

2.1 A Laboratory Experiment

2.2 Analyzing the Experiment

2.3 Some Statistical Considerations

2.3.1 Framing the Hypothesis

2.3.2 Hypothesis Versus Alternative

2.3.3 Unpredictable Variation

2.4 Two Types of Populations

2.4.1 Predicting the Unpredictable

2.5 Binomial Outcomes

2.5.1 Permutations and Rearrangements

2.5.2 Back to the Binomial

2.5.3 Probability

2.6 Independence

2.7 Selecting the Sample

2.7.1 A Young Woman Tasting Herbal Tea

2.7.2 Random Sampling and Representative Samples

2.8 Summary

2.9 To Learn More

2.10 Exercises

3 Testing Hypotheses

3.1 Five Steps to a Permutation Test

3.1.1 Analyze the Problem

3.1.2 Choose a Test Statistic

3.1.3 Compute the Test Statistic

3.1.4 Rearrange the Observations

3.1.5 Draw a Conclusion

3.2 A Second Example

3.2.1 Missing Data

3.2.2 More General Hypotheses

3.2.3 Behrens–Fisher Problem

3.3 Comparing Variances

3.3.1 Unequal Sample Sizes

3.4 Pitman Correlation

3.4.1 Effect of Ties

3.5 Bivariate Dependence

3.6 One-Sample Tests

3.6.1 The Bootstrap

3.6.2 Permutation Test

3.7 Matched Pairs

3.7.1 An Example: Pre-Treatment and Post-Treatment Levels

3.8 Summary

3.9 To Learn More

3.10 Exercises

4 When the Distribution Is Known

4.1 Properties of Independent Observations

4.2 Binomial Distribution

4.2.1 Testing Hypotheses About a Binomial Distribution

4.3 Poisson: Events Rare in Time and Space

4.3.1 Applying the Poisson

4.3.2 Comparing Two Poissons

4.3.3 Exponential Distribution

4.4 Normal Distribution

4.4.1 Tests for Location

4.4.2 Tests for Scale

4.5 Distributions

4.6 Applying What You've Learned

4.7 Summary and Further Readings

4.8 Exercises

5 Estimation

5.1 Point Estimation

5.2 Interval Estimation

5.2.1 Detecting an Unfair Coin

5.2.2 Confidence Bounds for a Sample Median

5.2.3 Confidence Intervals and Rejection Regions

5.2.4 One-Sample Test for the Variance

5.2.5 Behrens–Fisher Problem

5.3 Bivariate Correlation

5.4 Improving the Bootstrap Estimates

5.4.1 Bias-Corrected Bootstrap

5.4.2 Smoothing the Bootstrap

5.4.3 Iterated Bootstrap

5.5 Summary

5.6 To Learn More

5.7 Exercises

6 Power of a Test

6.1 Fundamental Concepts

6.1.1 Two Types of Error

6.1.2 Losses

6.1.3 Significance Level and Power

6.1.4 Exact, Unbiased Tests

6.1.5 Dollars and Decisions

6.1.6 What Significance Level Should I Use?

6.2 Assumptions

6.2.1 Transformations

6.3 How Powerful Are Our Tests?

6.3.1 One-Sample

6.3.2 Matched Pairs

6.3.3 Two Samples

6.4 Which Test?

6.5 Summary

6.6 To Learn More

6.7 Exercises

7 Categorical Data

7.1 Fisher's Exact Test

7.1.1 One-Tailed and Two-Tailed Tests

7.1.2 The Two-Tailed Test

7.2 Odds Ratio

7.2.1 Stratified 2 x 2's

7.3 Exact Significance Levels

7.4 Unordered r x c Contingency Tables

7.4.1 Causation Versus Association

7.5 Ordered Statistical Tables

7.5.1 Ordered 2 x c Tables

7.5.2 More than Two Rows and Two Columns

7.6 Summary

7.7 To Learn More

7.8 Exercises

8 Experimental Design and Analysis

8.1 Noise in the Data

8.1.1 Blocking

8.1.2 Measuring Factors We Can't Control

8.1.3 Randomization

8.2 k-Sample Comparison

8.2.1 Analyzing a One-Way Table

8.3 Balanced Designs

8.3.1 Main Effects

8.3.2 Analyzing a Two-Way Table

8.3.3 Testing for Interactions

8.3.4 A Worked-Through Example

8.4 Designing an Experiment

8.4.1 Latin Square

8.5 Determining Sample Size

8.6 Unbalanced Designs

8.6.1 Multidimensional Contingency Tables

8.6.2 Missing Combinations

8.7. Summary

8.8. To Learn More

8.9. Exercises

9 Multiple Variables and Multiple Hypotheses

9.1 Single-Valued Test Statistics

9.1.1 Two-Sample Multivariate Comparison

9.1.2 Applications to Repeated Measures

9.2 Combining Univariate Tests

9.3 The Generalized Quadratic Form

9.3.1 Mantel's U

9.3.2 Example in Epidemiology

9.3.3 Further Generalization

9.3.4 The MRPP Statistic

9.4 Multiple Hypotheses

9.5 Summary

9.6 To Learn More

9.7 Exercises

10 Model Building

10.1 Picturing Relationships

10.2 Unpredictable Variation

10.2.1 Building a Model

10.2.2 Estimating the Parameters

10.2.3 Testing for Significance

10.2.4 Comparing Two Regression Lines

10.3 Limitations of Regression

10.3.1 Local Regression

10.4 Validation

10.4.1 Metrics

10.4.2 Cross-Validation

10.5 Prediction Error

10.5.1 Correcting for Bias

10.6 Multivariate Relationships

10.6.1 Limitations of Multiple Regression

10.6.2 Applied Marketing Research

10.6.3 Transformations

10.7 Data Mining

10.7.1 Classification

10.7.2 Combined Approach

10.8 Summary

10.9 To Learn More

10.10 Exercises

11 Which Statistics Should I Use?

11.1 Parametric Versus Nonparametric

11.2 But Is It a Normal Distribution?

11.3 Which Hypothesis?

11.4 A Guide to Selection

11.4.1 Data in Categories

11.4.2 Ordered Observations

11.4.3 Continuous Data

Appendix 1 Program Your Own Resampling Statistics

Appendix 2 C++, SC, and Stata Code for Permutation Tests

Appendix 3 Resampling Software

Bibliography

Index

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May I remind you,this book is free on the internet.