TITLE:
Average Life Prediction Based on Incomplete Data
AUTHORS:
Tang Tang, Lingzhi Wang, Faen Wu, Lichun Wang
KEYWORDS:
Prediction Interval, Incomplete Data, Bayes Method, Two-Parameter Exponential Distribution
JOURNAL NAME:
Applied Mathematics,
Vol.2 No.1,
January
30,
2011
ABSTRACT: The two-parameter exponential distribution can often be used to describe the lifetime of products for example, electronic components, engines and so on. This paper considers a prediction problem arising in the life test of key parts in high speed trains. Employing the Bayes method, a joint prior is used to describe the variability of the parameters but the form of the prior is not specified and only several moment conditions are assumed. Under the condition that the observed samples are randomly right censored, we define a statistic to predict a set of future samples which describes the average life of the second-round samples, firstly, under the condition that the censoring distribution is known and secondly, that it is unknown. For several different priors and life data sets, we demonstrate the coverage frequencies of the proposed prediction intervals as the sample size of the observed and the censoring proportion change. The numerical results show that the prediction intervals are efficient and applicable.