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A note on kernel assisted estimators in missing covariate regression

✍ Scribed by Suojin Wang; C.Y. Wang

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
129 KB
Volume
55
Category
Article
ISSN
0167-7152

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✦ Synopsis

We investigate the asymptotic relationships among three kernel assisted semiparametric estimators in regression analysis when some covariates are missing or measured with error. Smoothing techniques are employed in estimating the selection probabilities and the conditionally expected scores, a step which is required to obtain the estimators of interest. The asymptotic distributional properties of these estimators are derived and their asymptotic equivalence is shown. Some important di erences are also noted. Furthermore, the asymptotic e ciency of the estimators relative to the usual maximum likelihood estimator is obtained.

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