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ML– and semiparametric estimation in logistic models with incomplete covariate data

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  • Vanessa Didelez

Abstract

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.

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  • Vanessa Didelez, 2002. "ML– and semiparametric estimation in logistic models with incomplete covariate data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(3), pages 330-345, August.
    • Handle: RePEc:bla:stanee:v:56:y:2002:i:3:p:330-345
    DOI: 10.1111/1467-9574.t01-1-00059
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