• Honam Mathematical Journal
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• v.26 no.2
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• pp.219-236
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• 2004

In this paper, the problem of estimating a p-variate ($p{\geq}4$) normal mean vector is considered in a decision-theoretic setup. Using a simple property of the noncentral chi-square distribution, a sequence of smooth estimators dominating the Lindley type estimator has been produced and each improved estimator is better than previous one.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.11-19
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• 2002

In this paper, the problem of estimating a p-variate $(p\geq4)$ normal mean vector in a decision-theoretic setup is considered. Using a technique of Guo and Pal (1992), a sequence of estimators dominating the Lindley type estimator is derived and each improved estimator is better than the previous one.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.463-472
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• 2005

In this paper, the problem of estimating a p-variate (p $\ge$4) normal mean vector is considered in decision-theoretic set up. Using a simple property of the noncentral chi-square distribution, a sequence of estimators dominating the Lindley type estimator with the cases of unknown covariance matrices has been produced and each improved estimator is better than previous one.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.355-371
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• 2018

The two parameter negative exponential distribution has many practical applications in queuing theory such as the service times of agents in system, the time it takes before your next telephone call, the time until a radioactive practical decays, the distance between mutations on a DNA strand, and the extreme values of annual snowfall or rainfall; consequently, has many applications in reliability systems. This paper considers an estimation problem of stress-strength model with two parameter negative parameter exponential distribution. We introduce a maximum penalized likelihood method, Bayes estimator using Lindley approximation to estimate stress-strength model and compare the proposed estimators with regular maximum likelihood estimator for complete data. We also introduce a maximum penalized likelihood method, Bayes estimator using a Markov chain Mote Carlo technique for incomplete data. A Monte Carlo simulation study is performed to compare stress-strength model estimates. Real data is used as a practical application of the proposed model.