Monte-Carlo Evidence On Adaptive Maximum LIkelihood Estimation Of A Regression

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Abstract
This paper reports preliminary monte carla evidence on the fixed sample size properties of adaptive maximum likelihood(AML} estimates of a simple 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 AML estimators when these parameters are pre-selected or, alternatively, are determined by a databased bootstrap method.
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