The Annals of Statistics

Theory of Partial Likelihood

Wing Hung Wong

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Abstract

A general asymptotic theory is developed for the maximum likelihood estimator based on a partial likelihood. Conditions are given for consistency and asymptotic normality, and a method is provided for the calculation of the asymptotic efficiency of the estimator. The implications of the general theory are examined in special cases such as inference in stochastic processes, Cox regression models, and AR processes with missing segments.

Article information

Source
Ann. Statist., Volume 14, Number 1 (1986), 88-123.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349844

Digital Object Identifier
doi:10.1214/aos/1176349844

Mathematical Reviews number (MathSciNet)
MR829557

Zentralblatt MATH identifier
0603.62032

JSTOR
links.jstor.org

Subjects
Primary: 62A10
Secondary: 62F12: Asymptotic properties of estimators 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62P10: Applications to biology and medical sciences

Keywords
Maximum likelihood estimator nuisance parameter Minimal Fisher Information martingale limit theorem missing values generalized autoregression nonstationary proportional hazard model conditional score

Citation

Wong, Wing Hung. Theory of Partial Likelihood. Ann. Statist. 14 (1986), no. 1, 88--123. doi:10.1214/aos/1176349844. https://projecteuclid.org/euclid.aos/1176349844


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