An Imputation Based Approach for Parameter Estimation in Reliability with Ambiguous Censoring
نویسنده
چکیده
This paper describes a novel approach based on “proportional imputation” when identical units produced in a batch have random but independent installation as well as failure time. The current problem is motivated from a real life industrial productiondelivery system when identical units are shipped after production to a third party warehouse and then sold at a future date for possible installation. Due to practical limitation, at any given time point, exact installation as well as failure time are known for only those units which have failed within that time frame after the installation. Hence in house reliability engineers are presented only with a very limited as well as partial data to estimate different model parameters related to installation as well as failure distribution. In reality other units in the batch are generally ignored for lack of available statistical methodology, giving rise to gross miss-specification. In this paper we have introduced a likelihood based parametric and computationally efficient solution to overcome this problem with optimal usage of available information. Proposed methodology is also supported via extensive simulation and a real data example.
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