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Maximum Empirical Likelihood Estimation In A Heteroscedastic Linear Regression ModelWith Possibly Missing Responses

Authors:

Anton Schick ,

Department of Mathematical Sciences, Binghamton University, Binghamton, New York,, US
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Yilin Zhu

Department of Mathematical Sciences, Binghamton University, Binghamton, New York,, US
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Abstract

A heteroscedastic linear regression model is considered where responses are allowed to be missing at random and with the conditional variance modeled as a function of the mean response. Maximum empirical likelihood estimation is studied for an empirical likelihood with an increasing number of estimated constraints. The resulting estimator is shown to be asymptotically normal and can outperform the ordinary least squares estimator.

DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7791

DOI: http://doi.org/10.4038/sljastats.v5i4.7791
How to Cite: Schick, A. & Zhu, Y., (2014). Maximum Empirical Likelihood Estimation In A Heteroscedastic Linear Regression ModelWith Possibly Missing Responses. Sri Lankan Journal of Applied Statistics. 5(4), pp.209–226. DOI: http://doi.org/10.4038/sljastats.v5i4.7791
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Published on 15 Dec 2014.
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