Translator Disclaimer
2017 Parametrically guided local quasi-likelihood with censored data
Majda Talamakrouni, Anouar El Ghouch, Ingrid Van Keilegom
Electron. J. Statist. 11(2): 2773-2799 (2017). DOI: 10.1214/17-EJS1293

Abstract

It is widely pointed out in the literature that misspecification of a parametric model can lead to inconsistent estimators and wrong inference. However, even a misspecified model can provide some valuable information about the phenomena under study. This is the main idea behind the development of an approach known, in the literature, as parametrically guided nonparametric estimation. Due to its promising bias reduction property, this approach has been investigated in different frameworks such as density estimation, least squares regression and local quasi-likelihood. Our contribution is concerned with parametrically guided local quasi-likelihood estimation adapted to randomly right censored data. The generalization to censored data involves synthetic data and local linear fitting. The asymptotic properties of the guided estimator as well as its finite sample performance are studied and compared with the unguided local quasi-likelihood estimator. The results confirm the bias reduction property and show that, using an appropriate guide and an appropriate bandwidth, the proposed estimator outperforms the classical local quasi-likelihood estimator.

Citation

Download Citation

Majda Talamakrouni. Anouar El Ghouch. Ingrid Van Keilegom. "Parametrically guided local quasi-likelihood with censored data." Electron. J. Statist. 11 (2) 2773 - 2799, 2017. https://doi.org/10.1214/17-EJS1293

Information

Received: 1 March 2016; Published: 2017
First available in Project Euclid: 4 July 2017

zbMATH: 1371.62029
MathSciNet: MR3679909
Digital Object Identifier: 10.1214/17-EJS1293

JOURNAL ARTICLE
27 PAGES


SHARE
Vol.11 • No. 2 • 2017
Back to Top