Performance of Kibria’s methods in partial linear ridge regression model

House price prediction is important for the government, finance company, real estate sector and also the house owner. The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of house size, age of house, condition of house and also the location of the house.

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Performance of Kibria's Method for the Heteroscedastic Ridge Regression Model: Some Monte Carlo Evidence

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2012.712185 ◽

2013 ◽

Vol 43 (4) ◽

pp. 673-686 ◽

Cited By ~ 18

Author(s):

Muhammad Aslam

Keyword(s):

Monte Carlo ◽

Regression Model ◽

Ridge Regression

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Stroke Risk Assessment Using Ridge Regression Model

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) ◽

10.1109/icoei.2018.8553960 ◽

2018 ◽

Author(s):

R.S. Jeena ◽

A SukeshKumar

Keyword(s):

Risk Assessment ◽

Regression Model ◽

Ridge Regression ◽

Stroke Risk

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Modified One-Parameter Liu Estimator for the Linear Regression Model

Modelling and Simulation in Engineering ◽

10.1155/2020/9574304 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Adewale F. Lukman ◽

B. M. Golam Kibria ◽

Kayode Ayinde ◽

Segun L. Jegede

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Ridge Regression ◽

Real Life ◽

Liu Estimator ◽

Real Life Data ◽

Squared Prediction Error ◽

Simulation Results ◽

Better Than

Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. Theoretical comparisons, real-life application, and simulation results show that it consistently dominates the usual Liu estimator. Under some conditions, it performs better than the ridge regression estimators in the smaller MSE sense. Two real-life data are analyzed to illustrate the findings of the paper and the performances of the estimators assessed by MSE and the mean squared prediction error. The application result agrees with the theoretical and simulation results.

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