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 language | English |
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Number of pages | 24 |
Journal | Communications in Statistics: Theory and Methods |
Early online date | 13 Jan 2017 |
DOIs | |
Publication status | E-pub ahead of print - 13 Jan 2017 |
Keywords
- Convergence
- incomplete data
- empirical process
- kernel
- density