Title

Bias Reduction Using Stochastic Approximation

Publication Type

Journal Article

Publication Date

1998

Abstract

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.

Discipline

Economics

Research Areas

Econometrics

Publication

Australian and New Zealand Journal of Statistics

Volume

40

Issue

1

First Page

43

Last Page

52

ISSN

1369-1473

Identifier

10.1111/1467-842x.00005

Publisher

Wiley

Additional URL

http://dx.doi.org/10.1111/1467-842x.00005

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