Browsing by Author "Suraj Yadav"

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A new generalized class of Kavya–Manoharan distributions: inferences and applications
(Springer, 2024) Ela Verma; Sanjay Kumar Singh; Suraj Yadav
This article introduces a new method of generating distributions by leveraging the concept of generalization with the hope of achieving more flexibility and greater adaptability. As a baseline distribution, we have considered a one-parameter exponential distribution. Along with studying the behavior of hazard rate, we have explored various statistical characteristics of the proposed distribution. For estimating model parameters we have employed the method of maximum likelihood estimation. To check the empirical validation of estimators obtained, the Monte Carlo simulation technique has been used. To show the model’s flexibility and competency, we have conducted a real data analysis using three real data sets and compared its performance with some widely used existing distributions. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
A new generalized class of Kavya–Manoharan distributions: inferences and applications
(Springer, 2025) Ela Verma; Sanjay Kumar Singh; Suraj Yadav
This article introduces a new method of generating distributions by leveraging the concept of generalization with the hope of achieving more flexibility and greater adaptability. As a baseline distribution, we have considered a one-parameter exponential distribution. Along with studying the behavior of hazard rate, we have explored various statistical characteristics of the proposed distribution. For estimating model parameters we have employed the method of maximum likelihood estimation. To check the empirical validation of estimators obtained, the Monte Carlo simulation technique has been used. To show the model’s flexibility and competency, we have conducted a real data analysis using three real data sets and compared its performance with some widely used existing distributions. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
Parameter estimation of Burr type-III distribution under generalized progressive hybrid censoring scheme
(Springer, 2024) Suraj Yadav; Sanjay Kumar Singh; Arun Kaushik
In this article, a generalised progressive hybrid censoring scheme is used to estimate the lifetime characteristics of the Burr type III distribution. Both classical and Bayesian inferential procedures are developed to estimate the parameters of the considered model. The maximum likelihood estimators and their asymptotic confidence intervals for the parameters in the classical framework are derived. Additionally, Bayes estimators under symmetric and asymmetric loss functions are derived using independent gamma priors. The Markov chain Monte Carlo technique is implemented to compute the posterior expectations. Moreover, the expected total time of the test and the expected number of failures are also computed. Next, a Monte Carlo simulation study is performed to assess the performance of the proposed estimators. Two real datasets are analysed to demonstrate the practical applicability of the study. The results show that both classical and Bayesian inferential procedures perform satisfactorily and that the Bayesian results outperform the traditional classical. © The Author(s) under exclusive licence to Japanese Federation of Statistical Science Associations 2024.
Parameter estimation of Burr type-III distribution under generalized progressive hybrid censoring scheme
(Springer, 2025) Suraj Yadav; Sanjay Kumar Singh; Arun Kaushik
In this article, a generalised progressive hybrid censoring scheme is used to estimate the lifetime characteristics of the Burr type III distribution. Both classical and Bayesian inferential procedures are developed to estimate the parameters of the considered model. The maximum likelihood estimators and their asymptotic confidence intervals for the parameters in the classical framework are derived. Additionally, Bayes estimators under symmetric and asymmetric loss functions are derived using independent gamma priors. The Markov chain Monte Carlo technique is implemented to compute the posterior expectations. Moreover, the expected total time of the test and the expected number of failures are also computed. Next, a Monte Carlo simulation study is performed to assess the performance of the proposed estimators. Two real datasets are analysed to demonstrate the practical applicability of the study. The results show that both classical and Bayesian inferential procedures perform satisfactorily and that the Bayesian results outperform the traditional classical. © The Author(s) under exclusive licence to Japanese Federation of Statistical Science Associations 2024.
Statistical properties and estimation procedures for a new flexible two parameter lifetime distribution
(Gnedenko Forum, 2022) S.K. Singh; Suraj Yadav; Abhimanyu Singh Yadav
In this article, a new transformation technique based on the cumulative distribution function is proposed, the proposed transformation technique is very useful to generate a class of lifetime distribution. The various statistical properties of the proposed transformation method are studied. Further, the proposed technique is illustrated by considering exponential distribution as a baseline distribution. Various statistical properties such as survival and hazard rate, moments, mean deviation about mean and median, order statistics, moment generating function (MGF), Bonferroni’s, and Lorenz curves, entropy, stress-strength reliability have been discussed. Different classical estimation methods are used to estimate the unknown parameters. Finally, two real data sets are considered to justify the use of the proposed distribution in real scenario. © 2022 Reliability: Theory and Applications. All rights reserved.
Stress-strength reliability estimation of xgamma distribution under generalized progressive hybrid censoring scheme
(Springer, 2025) Suraj Yadav; Sanjay Kumar Singh; Abhimanyu Singh Yadav
This article aims to estimate the parameters and stress-strength reliability R=P(Y