Inverse probability weighted estimation in a reliability model with missing data
Year: 2011
Type: preprint
Abstract: The linear transformation model is a useful regression model for the analysis of reliability data. In this paper, we consider the problems of estimation and testing in this model in a context of missing data. Precisely, we consider the situation where explanatory variables are available for every unit in the experiment sample, while the event times and censoring indicators can only be observed on a subset of the sample. We rely on an inverse probability weighted-type estimation approach for approximating the regression parameter of interest. The theoretical and numerical properties of the resulting estimator are investigated. The proposed approach appears to outperform the classical complete-case analysis.
Source: HAL
Institution: Laboratoire Mathématiques, Image et Applications
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4290646996