• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.37-42
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• 2006

We shall consider an inference of the reliability P(Y

• Journal of Applied Reliability
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• v.3 no.1
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• pp.83-92
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• 2003

We studied the trend change of failure rate function and uncertainty of residual life function in terms of location of their trend change points. It is shown that the trend change of uncertainty of residual life takes place before the failure rate changes its trend. Like DIFR(IDFR) does not necessary implies IDMRL(DIMRL), we find the fact that DIFR(IDFR) does not always imply IDURL(DIURL) under certain conditions, through the exponentiated-weibull distribution.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.565-574
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• 2011

The exponenetiated distribution has been used in reliability and survival analysis especially when the data is censored. In this paper, we derive Bayesian estimation of shape parameter and reliability function in the exponenetiated half triangle distribution based on Type-I hybrid censored data. Here we consider conjugate prior and noninformative prior and obtained corresponding posterior distributions. As an illustration, the mean square errors of the estimates are computed. Comparisons are made between these estimators using Monte Carlo simulation study.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.353-365
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• 2017

In this article, we proposed a new three-parameter distribution called generalized half-logistic Poisson distribution with a failure rate function that can be increasing, decreasing or upside-down bathtub-shaped depending on its parameters. The new model extends the half-logistic Poisson distribution and has exponentiated half-logistic as its limiting distribution. A comprehensive mathematical and statistical treatment of the new distribution is provided. We provide an explicit expression for the $r^{th}$ moment, moment generating function, Shannon entropy and $R{\acute{e}}nyi$ entropy. The model parameter estimation was conducted via a maximum likelihood method; in addition, the existence and uniqueness of maximum likelihood estimations are analyzed under potential conditions. Finally, an application of the new distribution to a real dataset shows the flexibility and potentiality of the proposed distribution.