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Semiparametric quasi-likelihood estimation with missing data

Francesco Bravo () and David Jacho-Chávez ()

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 5, 1345-1369

Abstract: This article develops quasi-likelihood estimation for generalized varying coefficient partially linear models when the response is not always observable. This article considers two estimation methods and shows that under the assumption of selection on the observables the resulting estimators are asymptotically normal. As an application of these results this article proposes a new estimator for the average treatment effect parameter. A simulation study illustrates the finite sample properties of the proposed estimators.

Date: 2016
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DOI: 10.1080/03610926.2013.863928

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