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On density and regression estimation with incomplete data

Author

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  • Majid Mojirsheibani
  • Kevin Manley
  • William Pouliot

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.

Suggested Citation

  • Majid Mojirsheibani & Kevin Manley & William Pouliot, 2017. "On density and regression estimation with incomplete data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(23), pages 11688-11711, December.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:23:p:11688-11711
    DOI: 10.1080/03610926.2016.1277751
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