1403/01/09
اکبر اصغر زاده

اکبر اصغر زاده

مرتبه علمی: استاد
ارکید:
تحصیلات: دکترای تخصصی
اسکاپوس:
دانشکده: دانشکده علوم ریاضی
نشانی:
تلفن: 011-54302476

مشخصات پژوهش

عنوان
Best prediction regions for future exponential record intervals
نوع پژوهش
JournalPaper
کلیدواژه‌ها
Constrained optimization; exponential distribution; prediction region; record data; type-II censored data
سال
2020
مجله STATISTICS
شناسه DOI
پژوهشگران Elham Basiri ، Arturo Fernadez ، Akbar Asgharzadeh ، S. F. Bagheri

چکیده

A class of prediction regions for a future upper record interval (Rs, Rl) based on a type-II censored sample from the exponential distribution is presented in this paper. The best prediction region for (Rs, Rl) is then determined by solving a constrained nonlinear optimization problem. The objective function is the area of the prediction region and the constraints are related to the desired confidence level. According to our approach, it suffices to simultaneously solve four nonlinear equations for deriving the prediction region with minimal area. To show the usefulness of our results, we present a simulation study. Three practical studies regarding times between consecutive telephone calls, lifetimes to breakdown of insulating fluids and annual rainfalls recorded at Los Angeles Civic Center are provided for comparing conservative and optimal prediction regions. In most cases, the reduction in area is appreciable. Finally, some applications and extensions are also pointed out.