Estimating the parameters of two-parameter exponential distribution under random censoring with the presence of outlier data and determining the warranty period related to product quality.

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Nasiri Parviz; Guderzi Masoumi Fateme; Yarmohammadi Masoud

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Journal Paper

Paper Information

Journal: Journal of Quality Engineering and Management Year:2022 | Volume:12 | Issue:1 Page(s): 15-38

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Information Journal Paper

Title

Estimating the parameters of two-parameter exponential distribution under random censoring with the presence of outlier data and determining the warranty period related to product quality.

Author(s)

Nasiri Parviz | Guderzi Masoumi Fateme | Yarmohammadi Masoud | Issue Writer Certificate

Keywords

Random censoring

maximum likelihood estimation

Bayesian Estimation

Outlier Data

Mean Squared Error

Abstract

The two parameter exponential distribution is particularly important among statistical distributions due to its constant failure rate and has applications in the fields of medicine, biology, clinical trials, public health, engineering, economics, demographics, and life span data, and reliability. Due to the importance of life span data, two parameter exponential distribution with censored data has recently attracted the attention of many researchers, but so far the inference about the location parameter with random censored data in the presence of Outlier Data has not been discussed. In this article, the location and scale parameters of two parameter exponential distribution under Random censoring with the presence of k outliers are estimated by Bayesian and classical methods. Due to the importance of the spatial parameter, when censoring the two parameter exponential distribution with the presence of Outlier Data, the spatial parameter is considered the same but the scale parameter is different. In the Bayesian Estimation of parameters, the is checked using Gibbs sampling under the error squared loss function. We recommend used the Bayesian Estimation.The generalized variance is given according to the dimensions of the parameters using the maximum likelihood method.

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