000			02844cam a2200313 a 4500
003			EG-GiCUC
008			180116s2017 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.2017.Ra.O
100	0		_aRasha Saber Elshaarawy Elbagoury
245	1	0	_aOn the weibull-G family of distributions / _cRasha Saber Elshaarawy Elbagoury ; Supervised Mohamed A. H.Sabry , Hiba Zeyada Muhammed
246	1	5	_aG- عن توزيعات عائلة وابيل
260			_aCairo : _bRasha Saber Elshaarawy Elbagoury , _c2017
300			_a117 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Mathematical Statistics
520			_aFitting distributions to data is the procedure of selecting a statistical distribution that best fits that data set generated by some random process. There are lots of probability distributions that can fit your data but the question is {u2018}how good can the distribution fits the data?{u2019}. To answer this question, several authors have proposed new probability distributions. Either by adding a new parameter to make the new distribution more flexible to catch as much as possible information from the data or by mixing two or more distributions together with some criteria and weights or generating new families of distributions using the concept of composite functions. In this thesis, we display the importance of generating families from numerous classical distributions in lifetime data analysis. We emphasize our work on the Weibull-G family of distributions and study its properties. As a special case, we study the three-parameter continuous model called the Weibull exponential (WE) distribution which can be seen as an extension of the Weibull distribution and discuss some of its interesting structural properties. The model parameters are estimated by the maximum likelihood estimation (MLE) method under both simple random sampling (SRS) and ranked set sampling (RSS) schemes. Simulation study is conducted to assess the performance of the model under different parameters values. One application of the new model to real data shows that it can be more reliable distribution tin modeling lifetime data than existing important lifetime models. Also, estimation of the model parameter under the RSS scheme provides more efficient estimators than those estimated under the SRS scheme
530			_aIssued also as CD
653		4	_aSimple random sampling
653		4	_aWeibull exponential
653		4	_aWeibull-G family
700	0		_aHiba Zeyada Muhammed , _eSupervisor
700	0		_aMohamed A. H.Sabry , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c64603 _d64603