PARAMETER ESTIMATION IN COX'S REGRESSION MODEL FOR COMPETING RISKS WITH MISSING FAILURE TYPES
نویسندگان
چکیده
منابع مشابه
Regression Analysis of Competing Risks Data with General Missing Pattern in Failure Types
In competing risks data, missing failure types (causes) is a very common phenomenon. In a general missing pattern, if a failure type is not observed, one observes a set of possible types containing the true type along with the failure time. Dewanji and Sengupta (2003) considered nonparametric estimation of the cause-specific hazard rates and suggested a Nelson-Aalen type estimator under such ge...
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A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the compet- ing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. ...
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This paper considers generalized linear quantile regression for competing risks data when the failure type may be missing. Two estimation procedures for the regression co-efficients, including an inverse probability weighted complete-case estimator and an augmented inverse probability weighted estimator, are discussed under the assumption that the failure type is missing at random. The proposed...
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Approved: Thesis Supervisor Title and Department
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We discuss efficient estimation in regression models that are defined by a finite-dimensional parametric constraint. This includes a variety of regression models, in particular the basic nonlinear regression model and quasi-likelihood regression. We are interested in the case where responses are missing at random. This is a popular research topic and various methods have been proposed in the li...
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ژورنال
عنوان ژورنال: JOURNAL OF THE JAPAN STATISTICAL SOCIETY
سال: 1995
ISSN: 1882-2754,1348-6365
DOI: 10.14490/jjss1995.25.81