On density and regression estimation with incomplete data

Majid Mojirsheibani, Kevin Manley, William Pouliot

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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 estimator 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 estimates of the unknown selection probabilities. We also address the problem of estimating a regression function with incomplete data.
Original languageEnglish
Number of pages24
JournalCommunications in Statistics: Theory and Methods
Early online date13 Jan 2017
DOIs
Publication statusE-pub ahead of print - 13 Jan 2017

Keywords

  • Convergence
  • incomplete data
  • empirical process
  • kernel
  • density

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