Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics

2021 ◽  
Author(s):  
Muhammad Amin ◽  
Muhammad Faisal ◽  
Muhammad Nauman Akram
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Inverse Gaussian ◽  
Influence Diagnostics

Influence diagnostics in gamma ridge regression model

2018 ◽  
Vol 89 (3) ◽  
pp. 536-556 ◽  
Author(s):  
Muhammad Amin ◽  
Muhammad Amanullah ◽  
Muhammad Aslam ◽  
Muhammad Qasim
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Influence Diagnostics

scholarly journals Modelling House Price Using Ridge Regression and Lasso Regression

2018 ◽  
Vol 7 (4.30) ◽  
pp. 498 ◽  
Author(s):  
Seng Jia Xin ◽  
Kamil Khalid
Keyword(s):  
Multivariate Analysis ◽  
Regression Model ◽  
Ridge Regression ◽  
House Price ◽  
Lasso Regression ◽  
Price Prediction ◽  
Real Estate Sector ◽  
Finance Company ◽  
The Government ◽  
House Condition

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.

scholarly journals Modified One-Parameter Liu Estimator for the Linear Regression Model

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.

A mixed poisson-inverse-gaussian regression model

1989 ◽  
Vol 17 (2) ◽  
pp. 171-181 ◽  
Author(s):  
C. Dean ◽  
J. F. Lawless ◽  
G. E. Willmot
Keyword(s):  
Regression Model ◽  
Inverse Gaussian ◽  
Gaussian Regression

Ridge Regression Model with Fuzzy Data

2020 ◽  
Vol 30 (5) ◽  
pp. 373-379
Author(s):  
Jae-Won Shim ◽  
Hye-Young Jung
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Fuzzy Data
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