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Semiparametric deconvolution with unknown error variance

  • William Horrace

    ()

  • Christopher Parmeter

    ()

Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about teh nature of the measurement error, more specifically, that the distribution is *entirely* known. We relax this assumption in the context of a regression error component model and develop an estimator for the unkinown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method.

(This abstract was borrowed from another version of this item.)

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File URL: http://hdl.handle.net/10.1007/s11123-010-0193-z
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Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 35 (2011)
Issue (Month): 2 (April)
Pages: 129-141

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Handle: RePEc:kap:jproda:v:35:y:2011:i:2:p:129-141
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