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Bias Reduction using Stochastic Approximation

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The paper studies stochastic approximation as a technique for bias reduction. The proposed method does not require approximating the bias explicitly, nor does it rely on having independent identically distributed (i.i.d.) data. The method always removes the leading bias term, under very mild conditions, as long as auxiliary samples from distributions with given parameters are available. Expectation and variance of the bias-corrected estimate are given. Examples in sequential clinical trials (non-i.i.d. case), curved exponential models (i.i.d. case) and length-biased sampling (where the estimates are inconsistent) are used to illustrate the applications of the proposed method and its small sample properties.
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Keywords: Bias; Robbins-Monro procedure; bootstrapping; jacknife; length-biased data; sequential analysis; stochastic approximation; stopping time

Document Type: Research Article

Affiliations: 1: Memorial Sloan-Kettering Cancer Center, New York, 2: CSIRO Mathematical and Information Sciences, Cleveland, Australia

Publication date: March 1, 1998

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