Statistical Simulation
S.M.T. K. MirMostafaee
Abstract
In this paper, we introduce a new extension of the XLindley distribution, called the exponentiated new XLindley distribution. The new model has an increasing or bathtub-shaped hazard rate function, making it suitable for modeling real-life phenomena. We study important properties of the ...
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In this paper, we introduce a new extension of the XLindley distribution, called the exponentiated new XLindley distribution. The new model has an increasing or bathtub-shaped hazard rate function, making it suitable for modeling real-life phenomena. We study important properties of the new model, such as the moments, moment generating function, incomplete moments, mean deviations from the mean and the median, Bonferroni and Lorenz curves, mean residual life function, Rényi entropy, order statistics, and k-record values. We also address the estimation of parameters using the maximum likelihood and bootstrap methods. A Monte Carlo simulation study is conducted to evaluate the estimators discussed in the paper. Additionally, we analyze two real data applications, including rainfall and COVID-19 data sets, to demonstrate the applicability and flexibility of the new distribution. Our results show that the new model fits the data sets better than several other recognized or recently introduced distributions, based on some well-known goodness-of-fit criteria.
Statistical Simulation
farzad eskandari
Abstract
Imprecise measurement tools produce imprecise data. Interval-valued data is usually used to deal with such imprecision. So interval-valued variables are used in estimation methods. They have recently been modeled by linear regression models. If response variable has any statistical distributions, interval-valued ...
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Imprecise measurement tools produce imprecise data. Interval-valued data is usually used to deal with such imprecision. So interval-valued variables are used in estimation methods. They have recently been modeled by linear regression models. If response variable has any statistical distributions, interval-valued variables are modeled in generalized linear models framework. In this article, we propose a new consistent estimator of a parameter in generalized linear models with regard to distributions of response variable in the exponential family. A simulation study shows that the new estimator is better than others on the basis of particular distributions of response variable. We present optimal properties of the estimators in this research
