On density and regression estimation with incomplete data

Majid Mojirsheibani; Kevin Manley; William Pouliot

doi:10.1080/03610926.2016.1277751

On density and regression estimation with incomplete data

Majid Mojirsheibani, Kevin Manley, William Pouliot

Economics

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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 language	English
Number of pages	24
Journal	Communications in Statistics: Theory and Methods
Early online date	13 Jan 2017
DOIs	https://doi.org/10.1080/03610926.2016.1277751
Publication status	E-pub ahead of print - 13 Jan 2017

Keywords

Convergence
incomplete data
empirical process
kernel
density

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Horvitz_Thompson_AA
This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Theory and Methods on 13/01/2017, available online: http://www.tandfonline.com/10.1080/03610926.2016.1277751
Accepted author manuscript, 681 KBLicence: Other (please specify with Rights Statement)

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AU - Mojirsheibani, Majid

AU - Manley, Kevin

AU - Pouliot, William

PY - 2017/1/13

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N2 - 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.

AB - 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.

KW - Convergence

KW - incomplete data

KW - empirical process

KW - kernel

KW - density

U2 - 10.1080/03610926.2016.1277751

DO - 10.1080/03610926.2016.1277751

M3 - Article

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JO - Communications in Statistics: Theory and Methods

JF - Communications in Statistics: Theory and Methods

ER -

On density and regression estimation with incomplete data

Abstract

Keywords

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