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.
semi-uniform consistency, error component, semiparametric deconvolution
Working Papers Series
Horrace, William C. and Parmeter, Christopher F., "Semiparametric Deconvolution with Unknown Error Variance" (2008). Center for Policy Research. 62.
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