Yazar "Belaghi, Reza Arabi" seçeneğine göre listele

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    Estimation in Weibull Distribution Under Progressively Typ e-I Hybrid Censored Data
    (Inst Nacional Estatistica-Ine, 2022) Asar, Yasin; Belaghi, Reza Arabi
    In this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively typ e-I hybrid censoring scheme. The Newton-Raphson algorithm, Expectation-Maximization (EM) algorithm and Stochastic EM algorithm are utilized to derive the maximum likelihood estimates for the unknown parameters. Moreover, Bayesian estimators using Tierney-Kadane Method and Markov Chain Monte Carlo method are obtained under three different loss functions, namely, squared error loss, linear-exponential and generalized entropy loss functions. Also, the shrinkage pre-test estimators are derived. An extensive Monte Carlo simulation experiment is conducted under different schemes so that the performances of the listed estimators are compared using mean squared error, confidence interval length and coverage probabilities. Asymptotic normality and MCMC samples are used to obtain the confidence intervals and highest posterior density intervals respectively. Further, a real data example is presented to illustrate the methods. Finally, some conclusive remarks are presented.
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    Improved shrinkage estimators in the beta regression model with application in econometric and educational data
    (Springer, 2023) Belaghi, Reza Arabi; Asar, Yasin; Larsson, Rolf
    Although beta regression is a very useful tool to model the continuous bounded outcome variable with some explanatory variables, however, in the presence of multicollinearity, the performance of the maximum likelihood estimates for the estimation of the parameters is poor. In this paper, we propose improved shrinkage estimators via Liu estimator to obtain more efficient estimates. Therefore, we defined linear shrinkage, pretest, shrinkage pretest, Stein and positive part Stein estimators to estimate of the parameters in the beta regression model, when some of them have not a significant effect to predict the outcome variable so that a sub-model may be sufficient. We derived the asymptotic distributional biases, variances, and then we conducted extensive Monte Carlo simulation study to obtain the performance of the proposed estimation strategy. Our results showed a great benefit of the new methodologies for practitioners specifically in the applied sciences. We concluded the paper with two real data analysis from economics and education.