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Handling outliers in seemingly unrelated regression equations model / Amr Ragab Rabie Kamel ; Supervised Ahmed Hassen Youssef , Mohamed Reda Abonazel

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Amr Ragab Rabie Kamel , 2021Description: 124 Leaves ; 30cmOther title:
  • معالجة القيم الشاذة فى نموذج معادلات الإنحدار التى تبدو غير مرتبطة [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 - Department of Statistics and Econometrics Summary: The concept of outliers for seemingly unrelated regressions equations (SURE) model is discussed and the consequences of outliers. Several approaches for detecting outliers are presented and shown to lead to the same diagnostic procedure.The problem of outliers is one of the oldest in econometrics and statistics, during the last century; interest in it has waxed and waned several times, one of the main problems in regression estimation methods. The SURE model is one of the econometric developments that have found considerable use in applied statistics.SURE model is one regression multivariate case, which have especial assumption, i.e., correlation between errors on the multivariate linear models (MLMs), by considering multiple regression equations that are linked by contemporaneously correlated disturbances. The assumptions underlying most SURE estimators give little consideration to influential observations, which may be present in the dataset. To overcome this problem, robust estimation is commonly applied to solve the problem caused by outliers.This thesis introduces a comparative study for some different robust estimators in SURE model.This is achieved by simulation study and empirical application to evaluate the robust estimator.The Monte Carlo simulation and application results indicate that the (non-robust) Ordinary Least Squares (OLS), Maximum Likelihood (ML) and Feasible Generalized Least Squares (FGLS) estimators are very sensitive to outliers, while robust (M-estimate, S-estimate and MM-estimate) estimators are more effective. In addition, MM-estimation method is more efficient, and the MM-estimator outperforms the other estimators in terms of relative absolute bias (RAB), total mean squared error (TMSE) and total mean absolute error (TMAE)
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.M.Sc.2021.Am.H (Browse shelf(Opens below)) Not for loan 01010110084033000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.M.Sc.2021.Am.H (Browse shelf(Opens below)) 84033.CD Not for loan 01020110084033000

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

The concept of outliers for seemingly unrelated regressions equations (SURE) model is discussed and the consequences of outliers. Several approaches for detecting outliers are presented and shown to lead to the same diagnostic procedure.The problem of outliers is one of the oldest in econometrics and statistics, during the last century; interest in it has waxed and waned several times, one of the main problems in regression estimation methods. The SURE model is one of the econometric developments that have found considerable use in applied statistics.SURE model is one regression multivariate case, which have especial assumption, i.e., correlation between errors on the multivariate linear models (MLMs), by considering multiple regression equations that are linked by contemporaneously correlated disturbances. The assumptions underlying most SURE estimators give little consideration to influential observations, which may be present in the dataset. To overcome this problem, robust estimation is commonly applied to solve the problem caused by outliers.This thesis introduces a comparative study for some different robust estimators in SURE model.This is achieved by simulation study and empirical application to evaluate the robust estimator.The Monte Carlo simulation and application results indicate that the (non-robust) Ordinary Least Squares (OLS), Maximum Likelihood (ML) and Feasible Generalized Least Squares (FGLS) estimators are very sensitive to outliers, while robust (M-estimate, S-estimate and MM-estimate) estimators are more effective. In addition, MM-estimation method is more efficient, and the MM-estimator outperforms the other estimators in terms of relative absolute bias (RAB), total mean squared error (TMSE) and total mean absolute error (TMAE)

Issued also as CD

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