Bias Reduction Using Stochastic Approximation
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 conditi...
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1998
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sg-smu-ink.soe_research-15042010-09-23T05:48:03Z Bias Reduction Using Stochastic Approximation Leung, Denis H. Y. Wang, Y. G. 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. 1998-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/505 info:doi/10.1111/1467-842x.00005 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Economics |
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Economics |
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Economics Leung, Denis H. Y. Wang, Y. G. Bias Reduction Using Stochastic Approximation |
| description |
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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text |
| author |
Leung, Denis H. Y. Wang, Y. G. |
| author_facet |
Leung, Denis H. Y. Wang, Y. G. |
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Leung, Denis H. Y. |
| title |
Bias Reduction Using Stochastic Approximation |
| title_short |
Bias Reduction Using Stochastic Approximation |
| title_full |
Bias Reduction Using Stochastic Approximation |
| title_fullStr |
Bias Reduction Using Stochastic Approximation |
| title_full_unstemmed |
Bias Reduction Using Stochastic Approximation |
| title_sort |
bias reduction using stochastic approximation |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
1998 |
| url |
https://ink.library.smu.edu.sg/soe_research/505 |
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1770569194841047040 |