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Statistical Inferences Based On Non-Smooth Estimating Functions

  • Lu Tian

    (Harvard University)

  • Jun Liu

    (Harvard University)

  • Mary Zhao

    (Harvard University)

  • L. J. Wei

    (Harvard University)

Registered author(s):

    When the estimating function for a vector of parameters is not smooth, it is often rather difficult, if not impossible, to obtain a consistent estimator by solving the corresponding estimating equation using standard numerical techniques. In this paper, we propose a simple inference procedure via the importance sampling technique, which provides a consistent root of the estimating equation and also an approximation to its distribution without solving any equations or involving nonparametric function estimates. The new proposal is illustrated and evaluated via two extensive examples with real and simulated datasets. Copyright 2004, Oxford University Press.

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    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1005&context=harvardbiostat
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    Paper provided by Berkeley Electronic Press in its series Harvard University Biostatistics Working Paper Series with number 1005.

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    Date of creation: 11 Jul 2004
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    Handle: RePEc:bep:hvdbio:1005
    Note: oai:bepress.com:harvardbiostat-1005
    Contact details of provider: Web page: http://www.bepress.com

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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