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- Title
Bias Reduction using Stochastic Approximation.
- Authors
Leung, Denis Heng-Yan; Wang, You-Gan
- 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.
- Subjects
STOCHASTIC approximation; APPROXIMATION theory; STOCHASTIC processes
- Publication
Australian & New Zealand Journal of Statistics, 1998, Vol 40, Issue 1, p43
- ISSN
1369-1473
- Publication type
Article
- DOI
10.1111/1467-842X.00005