CPR Working Paper Series No. 104
Semiparametric Deconvolution with Unknown Error Variance
William C. Horrace and Christopher F. Parmeter
April 2008
Abstract:
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 the 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 unknown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method.You can download a PDF version of the paper and view it and print it using a FREE copy of Adobe Acrobat Reader.
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