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ML– and semiparametric estimation in logistic models with incomplete covariate data
Author(s) -
Didelez Vanessa
Publication year - 2002
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.t01-1-00059
Subject(s) - covariate , semiparametric regression , estimator , semiparametric model , econometrics , parametric statistics , logistic regression , mathematics , estimation , statistics , simple (philosophy) , parametric model , economics , management , philosophy , epistemology
ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.