Browsing by Author "Prashant Kumar Chaurasia"

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A new asymmetric loss function for estimation of any parameter
(DAV College, 2020) Dinesh Kumar; Pawan Kumar; Pradip Kumar; Umesh Singh; Prashant Kumar Chaurasia
A new asymmetric loss function which is suitable for estimation of location as well as scale and other parameters has been introduced. To check the superiority of the proposed loss function over some existing and exploited loss functions such as squared error loss function (SELF), general entropy loss function (GELF), LINEX loss function and Logarithmic-SELF (LSELF), we have calculated the Bayes estimators of the parameterθ of exponential distribution under SELF, GELF, LINEX loss function, Logarithmic- SELF (LSELF) and the proposed exponential squared error loss function (ESELF) for complete sample from the exponential distribution. A data set has been considered to show its application to the real problems. The simulation study is carried out to compare the performance of Bayes estimators in terms of their posterior risks. © 2020 DAV College. All rights reserved.
A new lifetime distribution: Some of its statistical properties and application
(Natural Sciences Publishing, 2018) Dinesh Kumar; Umesh Singh; Sanjay Kumar Singh; Prashant Kumar Chaurasia
In the present paper, a new lifetime distribution has been introduced by the use of Minimum Guarantee transformation as suggested by Kumar et al. (2017). For the purpose, Lindley distribution is considered as a baseline distribution. Some of the statistical properties of this distribution has been studied and classical estimators like maximum likelihood estimator (MLE), least square estimator (LSE) and maximum product of spacing estimator (MPSE) has been obtained and their performance is carried out through simulation study. Further, a real data has been taken to show its application in the real scenario. ©2018 NSP Natural Sciences Publishing Cor.
ESTIMATION AND APPLICATION OF A NEW GENERALIZATION OF EXPONENTIAL DISTRIBUTION
(Gnedenko Forum, 2025) Deepak Kumar; Prashant Kumar Chaurasia; Pawan Kumar; A. Sahoo
In statistical literature, several lifetime distributions exist for real phenomena. And one of the methods to find new lifetime distribution by existing baseline distribution such a method is known as the transformation method. In this article, we proposed a generalization of the existing transformation by introducing the additional shape parameter. Here, we considered baseline distributions as an exponential distribution. Various statistical properties of the new lifetime distribution, such as survival function, hazard rate function, cumulative hazard rate function, moments, quantile function, and order statistics, have been discussed. Demonstrate the applicability and suitability of the proposed distribution. Here, we focus only on the estimation of the parameters likes MLE, LSE and also to check long-run behaviour of the estimators. © 2025, Gnedenko Forum. All rights reserved.