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040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.04.M.Sc.2021.Mo.S
100	0		_aMohamed Cherif Ali Yassin
245	1	0	_aStudying the efficiency of variable selection methods in econometric models / _cMohamed Cherif Ali Yassin ; Supervised Ahmed Amin Elsheikh , Mohamed Reda Abonazel
246	1	5	_aدراسة كفاءة طرق الإختيار المتغيرة فى نماذج الاقتصاد القياسى
260			_aCairo : _bMohamed Cherif Ali Yassin , _c2021
300			_a97 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics
520			_aModel selection methods in regression analysis have statistical value, especially the case of the models with multiple independent variables and then-recent developments in model selection methods to extract useful information from large databases (Big Data) in all fields. However, traditional statistical methods are unable to manage this bases of big data. Extracting useful information from these complex and informative rules has become a major challenge.The summary of this thesis is to compare between classical variable selection methods like ordinary least square (OLS), least absolute shrinkage and selection operator (LASSO), random forests, principle component analysis with neural network and two proposed methods are random forests with neural network and LASSO with neural network in Monte Carlo simulation study and application in real data with criteria (MSE, MAE and RMSE)
530			_aIssued also as CD
653		4	_aEconometric models
653		4	_aOrdinary least square (OLS)
653		4	_aVariable selection methods
700	0		_aAhmed Amin Elsheikh , _eSupervisor
700	0		_aMohamed Reda Abonazel , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c82234 _d82234