26 آبان 1403
مراد عليزاده

مراد علیزاده

مرتبه علمی: استادیار
نشانی: دانشکده مهندسی سیستم های هوشمند و علوم داده - گروه آمار
تحصیلات: دکترای تخصصی / امار ریاضی
تلفن: 0
دانشکده: دانشکده مهندسی سیستم های هوشمند و علوم داده

مشخصات پژوهش

عنوان A Bivariate Extension to Exponentiated Inverse Flexible Weibull Distribution: Shock Model, Features, and Inference to Model Asymmetric Data
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
statistical model; Marshall–Olkin shock model; marginal distributions; simulation; comparative study; statistics and numerical data
مجله SYMMETRY-BASEL
شناسه DOI
پژوهشگران محمود المرشدی (نفر اول) ، M. S. Eliwa M. S. Eliwa محمد (نفر دوم) ، محمد طاهیر (نفر سوم) ، مراد علیزاده (نفر چهارم) ، رعنا الدسوکی (نفر پنجم) ، افراه البسلی (نفر ششم به بعد) ، هانا الغفاری (نفر ششم به بعد)

چکیده

The primary objective of this article was to introduce a new probabilistic model for the dis- cussion and analysis of random covariates. The introduced model was derived based on the Marshall– Olkin shock model. After proposing the mathematical form of the new bivariate model, some of its distributional properties, including joint probability distribution, joint reliability distribution, joint reversed (hazard) rate distribution, marginal probability density function, conditional probability density function, moments, and distributions for bothY = max{X1,X2} andW = min{X1,X2}, were investigated. This novel model can be applied to discuss and evaluate symmetric and asymmetric data under various kinds of dispersion. Moreover, it can be used as a probability approach to analyze different shapes of hazard rates. The maximum likelihood approach was utilized for estimating the parameters of the bivariate model. A simulation study was carried out to assess the performance of the parameters, and it was noted that the maximum likelihood technique can be used to generate consistent estimators. Finally, two real datasets were analyzed to illustrate the notability of the novel bivariate distribution, and it was found that the suggested distribution provided a better fit than the competitive bivariate models.