On density and regression estimation with incomplete data

Majid Mojirsheibani, Kevin Manley and William Pouliot

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 23, 11688-11711

Abstract: We consider the problem of estimation of a density function in the presence of incomplete data and study the Hellinger distance between our proposed estimators and the true density function. Here, the presence of incomplete data is handled by utilizing a Horvitz–Thompson-type inverse weighting approach, where the weights are the estimates of the unknown selection probabilities. We also address the problem of estimation of a regression function with incomplete data.

Date: 2017
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DOI: 10.1080/03610926.2016.1277751

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