New approaches in estimating linear regression model parameters in the presence of multicollinearity and outliers
In multiple linear regression models, the ordinary least squares (OLS) method has been the most popular technique for estimating parameters of model due to its optimal properties and ease of calculation. OLS estimator may fail when the assumption of independence is violated. This assumption can be v...
محفوظ في:
المؤلف الرئيسي: | Al-Mash, Mohammad Sabry Abo |
---|---|
التنسيق: | أطروحة |
اللغة: | English |
منشور في: |
2017
|
الموضوعات: | |
الوصول للمادة أونلاين: | http://eprints.utm.my/id/eprint/78208/1/MohammadSabryAboMFS2017.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Linear regression for data having multicollinearity, heteroscedasticity and outliers
بواسطة: Rasheed, Bello AbdulKadiri
منشور في: (2017) -
A robust ridge regression estimator in the presence of outliers and multicollinearity /
بواسطة: Marina Zahari
منشور في: (2001) -
Robust techniques for linear regression with multicollinearity and outliers
بواسطة: Mohammed, Mohammed Abdulhussein
منشور في: (2016) -
A Robust Ridge Regression For Multicollinearity Problem In The Presence Of Outliers In The Data
بواسطة: Nur Aqilah Binti Ferdaos -
Robust diagnostic and estimation for binary logistic regression model in the presence of multicollinearity and high leverage points
بواسطة: Ariffin @ Mat Zin, Syaiba Balqish
منشور في: (2018)