000			02951cam a2200325 a 4500
003			EG-GiCUC
005			20250223032422.0
008			191022s2019 ua f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aPh.D
099			_aCai01.18.03.Ph.D.2019.Ma.O
100	0		_aMahmoud Mohamed Mahmoud Elsehetry
245	1	0	_aOn half logistic generated family and some related distributions / _cMahmoud Mohamed Mahmoud Elsehetry ; Supervised Elsayed Ahmed Elsherpieny
246	1	5	_aعن العائلة النصف لوجستية المولدة و بعض التوزيعات المرتبطة بها
260			_aCairo : _bMahmoud Mohamed Mahmoud Elsehetry , _c2019
300			_a150 Leaves ; _c30cm
502			_aThesis (Ph.D.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Mathematical Statistics
520			_aMany statistical distributions have been extensively used and applied for modeling data in several areas such as engineering, actuarial, medical sciences, demography, etc. However, in many situations, the classical distributions are not suitable for describing and predicting real world phenomena. For that reason, attempts have been made to define new techniques for creating new distributions by introducing additional shape parameter(s) to baseline model and at the same time provide great flexibility in modeling data in practice. The extended distributions have attracted the attention of many authors to expand new models because the computational and analytical facilities available in programming software such as R, Maple, and Mathematica can easily tackle the problems involved in computing special functions in these extended distributions. The aim of this thesis is to introduce and study two new generated families of distributions, namely; the Kumaraswamy type I half logistic generated family of distributions and the type II Kumaraswamy half logistic generated family of distributions by taking the half logistic distribution as a generator for two families with different transformation for each one. Also, a new distribution "as deeply study case" is introduced, which is applied on the first family. Furthermore, some statistical properties are derived and maximum likelihood estimation is applied. Four sub models in each family are explored. Simulation study for a particular distribution in each family is performed. The importance and flexibility of each family is assessed by applying it to real data sets and comparing it with other known distributions
530			_aIssued also as CD
653		4	_aGenerated Families of Distributions
653		4	_aHalf logistic distribution
653		4	_aMoment generating function
700	0		_aElsayed Ahmed Elsherpieny , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c74695 _d74695