Monte Carlo Simulation

by Christopher Z. Mooney

• ISBN13: 9780803959439
• ISBN10: 0803959435
• Edition: 1st
• Format: Paperback
• Copyright: 1997-04-07
• Publisher: Sage Publications, Inc

Summary

The author explains the logic behind the method and demonstrates its uses for social and behavioral research in: conducting inference using statistics with only weak mathematical theory; testing null hypotheses under a variety of plausible conditions; assessing the robustness of parametric inference to violations of its assumptions; assessing the quality of inferential methods; and comparing the properties of two or more estimators. In addition, Christopher Z Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and uses several research examples to demonstrate these principles.This volume will enable researchers to execute Monte Carlo Simulation effectively and to interpret the estimated sampling distribution generated from its use.

Table of Contents

Acknowledgments	v	(2)
Series Editor's Introduction	vii
1. Introduction	1	(4)
1.1 The Monte Carlo Principle	3	(2)
2. Generating Individual Samples From a Pseudo-Population	5	(45)
2.1 Setting Up a Population Generating Computer Algorithm	5	(2)
2.2 Generating Single Random Variables	7	(35)
2.3 Generating Combinations of Random Variables	42	(8)
3. Using the Pseudo-Population in Monte Carlo Simulation	50	(15)
3.1 An Example of a Complete Pseudo-Population Algorithm	50	(2)
3.2 Generating a Vector of Monte Carlo Estimates	52	(3)
3.3 Generating Multiple Experiments	55	(1)
3.4 Which Statistic Is to Be Saved From a Trial?	56	(1)
3.5 How Many Trials Are Needed?	57	(2)
3.6 Evaluating Monte Carlo Estimates of Sampling Distributions	59	(6)
4. Using Monte Carlo Simulation in the Social Sciences	65	(27)
4.1 Inference When Weak Statistical Theory Exists for an Estimator	66	(6)
4.2 Testing a Null Hypothesis Under a Variety of Plausible Conditions	72	(5)
4.3 Assessing the Quality of an Inference Method	77	(5)
4.4 Assessing the Robustness of Parametric Inference to Assumption Violations	82	(6)
4.5 Comparing Estimators' Properties	88	(4)
5. Conclusion	92	(5)
Notes	97	(2)
References	99	(4)
About the Author	103