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Efficient estimation in a nonlinear counting‐process regression model
Author(s) -
Greenwood P. E.,
Wefelmeyer W.
Publication year - 1991
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315795
Subject(s) - counting process , mathematics , estimator , censoring (clinical trials) , covariate , convolution (computer science) , distribution (mathematics) , combinatorics , statistics , discrete mathematics , mathematical analysis , computer science , artificial intelligence , artificial neural network
Suppose we observe i.i.d. copies of X, C, Y , where X is a counting process, C is a censoring process talcing only values 0 and 1, and Y is a covariate process. Assume that the intensity process of X is of the form C(s)a(s, Y(s)) with a unknown, but that the distribution of X, C, Y is unspecified otherwise. McKeague and Utikal proposed an estimator for the doubly cumulative hazard f f a(s, y) ds dy and determined its asymptotic distribution. We show that the estimator is regular and efficient in the sense of a Hájek‐Inagaki convolution theorem for partially specified models.