What question did this study set out to answer?

The study aims to evaluate the robustness of various 95% confidence interval procedures using Monte Carlo simulations.

June 1, 1976

Confidence Interval Robustness with Long-Tailed Symmetric Distributions

Key Points

The study aims to evaluate the robustness of various 95% confidence interval procedures using Monte Carlo simulations.
Applied Monte Carlo techniques to assess estimators with sample sizes of 10 and 20.
Tested a range of distributions from Gaussian to long-tailed Cauchy.
Evaluated robustness based on validity and efficiency of confidence intervals.
Identified several robust procedures with point M-estimators from the Princeton Robustness Study.
Determined the closeness of each estimator's level to the 5% target.
Compared expected lengths of confidence intervals across different estimators.

Abstract

Abstract A variety of 95-percent confidence interval procedures have been examined in some detail using Monte Carlo techniques. These estimators were tried on simulated samples of sizes 10 and 20 from a spectrum of distributions ranging from the Gaussian to the long-tailed Cauchy. The robustness of an estimator is measured by both the closeness of its level to the 5-percent goal (robustness of validity) and its expected length as compared to its competitors (robustness of efficiency). Results include some quite robust procedures including some of the point M-estimators from the Princeton Robustness Study.

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Confidence Interval Robustness with Long-Tailed Symmetric Distributions

Key Points

Abstract

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