Convergence Rate of K-Step Maximum Likelihood Estimate in Semiparametric Models
| dc.creator | Cheng, Guang | |
| dc.date | 2007-08-22 | |
| dc.date.accessioned | 2026-07-07T08:24:59Z | |
| dc.date.available | 2026-07-07T08:24:59Z | |
| dc.description | 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. | |
| dc.description | 22 pages, 2 tables | |
| dc.identifier | https://arxiv.org/abs/0708.3041 | |
| dc.identifier | http://arxiv.org/abs/0708.3041 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/136526 | |
| dc.subject | Statistics Theory | |
| dc.title | Convergence Rate of K-Step Maximum Likelihood Estimate in Semiparametric Models | |
| dc.type | text |