Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers

This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared t...

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Bibliographic Details
Main Authors: Pati, Kafi Dano, Adnan, Robiah, Rasheed, Bello Abdulkadir, MD. J., Muhammad Alias
Format: Conference Proceeding
Language:English
Subjects:
Economic models
Estimators
Outliers (statistics)
Parameter estimation
Parameter robustness
Regression
Regression analysis
Robustness (mathematics)
Root-mean-square errors
Standard error
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Summary:This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared these methods with existing estimators, namely ordinary least squares (OLS) and Huber ridge regression (HRID) using three criteria: Bias, Root Mean Square Error (RMSE) and Standard Error (SE) to estimate the parameters coefficients. The results of Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) are compared with existing methods using real data and simulation study. The empirical evidence shows that the results obtain from the BRLTS are the best among the three estimators followed by BRLAV with the least value of the RMSE for the different disturbance distributions and degrees of multicollinearity.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4954633