 Efficiency of estimators for partially specified filtered modelsP. E. Greenwood and W. Wefelmeyer Stochastic Processes and their Applications, 1990, vol. 36, issue 2, 353-370 Abstract: Let Xn1,..., Xnn be counting processes and let Yn1,..., Ynn be vector-valued covariate processes. Assume that the intensity processes of the Xni with respect to the filtration generated by Xni and Yni are known up to a (possibly infinite-dimensional) parameter, but that the distribution of Xni and Yni is unspecified otherwise. We give conditions under which the partially specified likelihood in the sense of Gill-Slud-Jacod is locally asymptotically normal. We show that the partially specified likelihood determines a covariance bound in the sense of a Hájek-LeCam convolution theorem for estimating functionals of the underlying parameter. The theorem shows that the Huffer-McKeague estimator is efficient in Aalen's additive risk model, and that the Cox estimator for the regression coefficients and a Breslow-type estimator for the integrated baseline hazard are efficient in Cox's and in Prentice and Self's proportional hazards models. Date: 1990 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:36:y:1990:i:2:p:353-370 Ordering information: This journal article can be ordered from Access Statistics for this article Stochastic Processes and their Applications is currently edited by T. Mikosch More articles in Stochastic Processes and their Applications from Elsevier |
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