000			029950000a22003490004500
003			EG-GICUC
005			20250223025219.0
008			091106s2004 ua f m 000 00eng d
040			_aEG-GICUC _beng _cEG-GICUC
041	0		_aEng
049			_aDeposite
097			_aM.Sc
099			_aCai01.03.01.M.Sc.2004.No.L.
100	0		_aNoha Ahmed Mohamed Youssef
245	1	0	_aLinear mixed effects model for longitudinal data with non - random dropout / _cNoha Ahmed Mohamed Youssef ; supervised Maged Osman , Thanaa Esmail
246	1	5	_aالنماذج الخطية ذات المؤثرات المختلطة للبيانات الطولية فى وجود قيم مفقودة نهائيا بصورة غير عشوائية
260			_aCairo : _bNoha Ahmed Mohamed Youssef , _c2004
300			_a89L ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty Of Economics and Political Science - Department Of Statistics
520			_aLongitudinal studies represent one of the principal research strategies employed in medical and social researchThey are the most appropriate studies for studying individual change through timeNon - random dropout is a common phenomenon associated with this type of dataLinear mixed effects model has been used for fitting longitudinal data in the presence of non - random dropoutIt offers a powerful tool for analyzing longitudinal dataIt gives us information about within individual variation and between individuals variationStandard methods of maximum likelihood estimation are intractable in the current setting , especially when missing data mechanism is taken into consideration , which means incorporating a model for the dropout mechanismThis incorporation increases the number of parameters needs to be estimatedThe selection model proposed by Diggle and Kenward (1994) has been followed in this study to model the dropout processIn this thesis , the stochastic EM algorithm has been applied for the first time to obtain the maximum likelihood estimates of the linear mixed effects model parameters in the case of non - random dropout besides the maximum likelihood estimates of the parameters that control the dropout processSince the result of this algorithm is a Markov chain , three methods of monitoring convergence have been used to assess convergence , which are Gelman and Rubin's method (1982) , Yu and Mykland's method (1998) and Brooks' method (1998) The bootstrap method has been used to compute the standard errors for the parameters estimatesAll these methods are applied to two real data sets
530			_aIssued also as CD
653		4	_aLinear mixed effects model
653		4	_aLongitudinal Data
653		4	_aMissing data ; Non - random dropout
653		4	_aThe Expectation Maximization algorithm
700	0		_aMaged Osman , _esupervisor
700	0		_aThanaa Esmail , _esupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aEnas _eCataloger
905			_aMustafa _eRevisor
942			_2ddc _cTH
999			_c8148 _d8148