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New biased estimators for seemingly unrelated regression equations model in case of incomplete data / Rehab Ahmed Abdalalim Abdalhaque ; Supervised Ahmed Amin Elsheikh , Mohamed Reda Abonazel

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Rehab Ahmed Abdalalim Abdalhaque , 2021Description: 108 Leaves : charts ; 30cmOther title:
  • مقدرات متحيزة جديدة لنموذج معادلات الإنحدار غير المرتبطة ظاهرياَ فى حالة البيانات غير المكتملة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research (FSSR) - Department of Statistics and Econometrics Summary: SURE model which was first proposed by Zellnerin 1962 became one of the most important regression models in the application aspects. In this thesis, we will estimate SURE model which suffers from missing values multicollinearity issues. This study is different in many aspects from other studies which estimated SURE model that handling the missing values and the multicollinearity under different factors such as sample size, percent of missing values, number of equations, number of explanatory variables and degree of correlation between the explanatory variables. All the previous factors are studied on different levels to know the effect of each level on the efficiency of the proposed estimators. For handling missing values in SURE model, four imputation approaches were introduced which are regression imputation; PMM; EM algorithm and MCMC. After that, we will handle multicollinearity problem by using Liu-Type estimation with Two-parameters and ridge estimation to estimate the bias. The Monte Carlo simulation study was done with 1000 replications.The results revealed that the EM-Liu type estimators were the best for imputing the missing values and handling the multicollinearity over all the factor and all levels in the terms of TMSE
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.M.Sc.2021.Re.N (Browse shelf(Opens below)) Not for loan 01010110085201000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.M.Sc.2021.Re.N (Browse shelf(Opens below)) 85201.CD Not for loan 01020110085201000

Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research (FSSR) - Department of Statistics and Econometrics

SURE model which was first proposed by Zellnerin 1962 became one of the most important regression models in the application aspects. In this thesis, we will estimate SURE model which suffers from missing values multicollinearity issues. This study is different in many aspects from other studies which estimated SURE model that handling the missing values and the multicollinearity under different factors such as sample size, percent of missing values, number of equations, number of explanatory variables and degree of correlation between the explanatory variables. All the previous factors are studied on different levels to know the effect of each level on the efficiency of the proposed estimators. For handling missing values in SURE model, four imputation approaches were introduced which are regression imputation; PMM; EM algorithm and MCMC. After that, we will handle multicollinearity problem by using Liu-Type estimation with Two-parameters and ridge estimation to estimate the bias. The Monte Carlo simulation study was done with 1000 replications.The results revealed that the EM-Liu type estimators were the best for imputing the missing values and handling the multicollinearity over all the factor and all levels in the terms of TMSE

Issued also as CD

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