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Monte Carlo Techniques in Studying Robust Estimators

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  • David C. Hoaglin

Abstract

Recent work on robust estimation has led to many procedures, which are easy to formulate and straightforward to program but difficult to study analytically. In such circumstances experimental sampling is quite attractive, but the variety and complexity of both estimators and sampling situations make effective Monte Carlo techniques essential. This discussion examines problems, techniques, and results and draws on examples in studies of robust location and robust regression.

Suggested Citation

  • David C. Hoaglin, 1973. "Monte Carlo Techniques in Studying Robust Estimators," NBER Working Papers 0016, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0016
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    References listed on IDEAS

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    1. Henry R. Neave, 1973. "On Using the Box‐Müller Transformation with Multiplicative Congruential Pseudo‐Random Number Generators," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 92-97, March.
    2. Paul W. Holland, 1973. "Weighted Ridge Regression: Combining Ridge and Robust Regression Methods," NBER Working Papers 0011, National Bureau of Economic Research, Inc.
    3. Paul W. Holland, 1973. "Monte Carlo for Robust Regression: The Swindle Unmasked," NBER Working Papers 0010, National Bureau of Economic Research, Inc.
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