Monte Carlo Evidence on Adaptive Maximum Likelihood Estimation of a Regression

David A. Hsieh, Charles F. Mansky, Charles Manski

Research output: Contribution to journalArticlepeer-review

Abstract

This paper reports Monte Carlo evidence on the fixed sample size properties of adaptive maximum likelihood estimates of a linear regression. The focus is on the problem of selecting the smoothing and trimming parameters used in estimating the score function. We examine the performance of adaptive maximum likelihood estimators when these parameters are preselected or, alternatively, are determined by a data-based bootstrap method.
Original languageEnglish
Pages (from-to)541-551
JournalThe Annals of Statistics
Volume15
StatePublished - 1987

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