000			02995cam a2200325 a 4500
003			EG-GiCUC
005			20250223032619.0
008			201024s2020 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.03.M.Sc.2020.Es.O
100	0		_aEssam Abdelsalam Abdelgawad Elbalakousy
245	1	0	_aOn the inverse weibull distribution / _cEssam Abdelsalam Abdelgawad Elbalakousy ; Supervised Hiba Zeyada Muhammed
246	1	5	_aعن توزيع وايبل المعكوس
260			_aCairo : _bEssam Abdelsalam Abdelgawad Elbalakousy , _c2020
300			_a101 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Mathematical Statistics
520			_aThe Weibull distribution is used widely to analyze the life testing data, but it cannot be used at all if the data indicate a non-monotone and uni-modal hazard function. Keller and Kamath (1982) introduced the inverse Weibull (IW) distribution with to parameters n and Ý to solve the Weibull distribution problem. The IW distribution is one of the famous distributions in analyzing the data from the reliability engineering and life testing experiment, it has been used to model, many real life applications for example degradation of mechanical components such as pistons and crankshafts of diesel engines.Recently, Mahdavi and Kundu (2017) introduced a new class of distributions by adding a new parameter to obtain a family of distributions this family is called the alpha power transformation (APT). Basheer (2019) had been used the method of the APT that proposed by Mahdavi and Kundu (2017) to introduce a new generalized alpha power inverse Weibull distribution. The alpha power inverse Weibull (APIW)distribution is more flexible over the IW distribution and it can be used to describe the data from the lifetime experiments, clinical trial and reliability engineering. In this thesis, we have studied the properties of the IW and APIW distributions, and then we have studied the problem of estimation for the unknown parameters for both distributions under random censoring data by using Bayesian and non Bayesian estimation methods. We computed the maximum likelihood estimates and Bayes estimates, for Bayes estimates we used the Markov Chain Monte Carlo (MCMC) techniques and we used the metropolis hasting algorithm, where it used when we can not directly sample from the conditional posterior distribution which we can not write an analytical expression for the posterior distribution
530			_aIssued also as CD
653		4	_aAlpha Power Inverse Weibull Distribution
653		4	_aInverse Weibull Distribution
653		4	_aRandom Censoring
700	0		_aHiba Zeyada Muhammed , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c78415 _d78415