Abstract
In this paper, the estimation for the bivariate generalized exponential (BVGE) distribution under type-I censored with constant stress accelerated life testing (CSALT) are discussed. The scale parameter of the lifetime distribution at constant stress levels is assumed to be an inverse power law function of the stress level. The unknown parameters are estimated by the maximum likelihood approach and their approximate variance-covariance matrix is obtained. Then, the numerical studies are introduced to illustrate the approach study using samples which have been generated from the bivariate generalized exponential distribution. Keywords : Accelerated life testing, Bivariate generalized exponential distribution, Constant stress, Type-I censoring, Maximum likelihood estimation.
Highlights
In many situations such as the case of the development of a new component or a product failure data at normal operating conditions are lacking and the reliability measure become difficult, if not impossible, to estimate
The estimation for the bivariate generalized exponential (BVGE) distribution under type-I censored with constant stress accelerated life testing (CSALT) are discussed
This paper presented statistical inference for BVGE distribution under CSALT with k-stress levels
Summary
In many situations such as the case of the development of a new component or a product failure data at normal operating conditions are lacking and the reliability measure become difficult, if not impossible, to estimate. The joint cumulative distribution function, the joint probability density function and the joint survival distribution function are in closed forms, which make it convenient to use in practice They used the method of maximum likelihood to estimate the parameters of the BVGE distribution from complete samples. Many authors presented bivariate distribution, Houggard et al [9] studied data on life length of Danish twins and Lin et al [14] considered data of colon cancer and the time from treatment to death. Kundu and Gupta [10] analyzed one data set International Journal of Advanced Statistics and Probability and observed that the proposed BVGE distribution provided a much better fit than the Marshal and Olkin bivariate exponential model. Attia et al [6] presented the maximum likelihood estimators for the unknown parameters of bivariate generalized linear failure rate distribution and their approximate variance-covariance matrix.
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