Convergence Rate of K-Step Maximum Likelihood Estimate in Semiparametric Models
Statistics Theory
2007-08-23 v1 Statistics Theory
Abstract
We suggest an iterative approach to computing K-step maximum likelihood estimates (MLE) of the parametric components in semiparametric models based on their profile likelihoods. The higher order convergence rate of K-step MLE mainly depends on the precision of its initial estimate and the convergence rate of the nuisance functional parameter in the semiparametric model. Moreover, we can show that the K-step MLE is as asymptotically efficient as the regular MLE after a finite number of iterative steps. Our theory is verified for several specific semiparametric models. Simulation studies are also presented to support these theoretical results.
Keywords
Cite
@article{arxiv.0708.3041,
title = {Convergence Rate of K-Step Maximum Likelihood Estimate in Semiparametric Models},
author = {Guang Cheng},
journal= {arXiv preprint arXiv:0708.3041},
year = {2007}
}
Comments
22 pages, 2 tables