scholarly journals A Novel S-Regression Model on an Auto Price

2018 ◽  
Vol 7 (2.29) ◽  
pp. 912
Author(s):  
Fadzilah Salim ◽  
Nur Azman Abu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Squared Error ◽  
Squared Error ◽  
Curve Model ◽  
Used Car ◽  
Independent Variable ◽  
The Mean ◽  
Used Cars ◽  
The Relationship

A simple linear regression model is useful in a prediction model. A general linear regression beyond a single independent variable is still not popular. A nonlinear regression can be easily produced a better predictive model but it is difficult to construct. The objective of this paper is to propose a technique for predicting the price of used cars in Malaysia using S-shaped curve model. In this paper, the S-shaped Membership Function [SMF] is used as the basis to develop a novel S-Regression model. Comparisons between linear regression, cubic regression and S-Regression have been made on the used car prices. The mean squared error of S-Regression model is found to be closer to cubic regression than the linear regression. S-Regression model is found to be quite suitable to represent the relationship between the price of a used car and the make year of a car. The result demonstrates that the S-Regression model gives better and practical estimate of the price of a used car in Malaysia.  

scholarly journals On the Stochastic Restrictedr-kClass Estimator and Stochastic Restrictedr-dClass Estimator in Linear Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jibo Wu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Multiple Linear Regression Model ◽  
Error Matrix ◽  
Squared Error ◽  
The Mean ◽  
Linear Restrictions ◽  
Theoretical Results

The stochastic restrictedr-kclass estimator and stochastic restrictedr-dclass estimator are proposed for the vector of parameters in a multiple linear regression model with stochastic linear restrictions. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a numerical example is given to show some of the theoretical results.

scholarly journals Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yalian Li ◽  
Hu Yang
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Principal Component ◽  
Monte Carlo Simulation Study ◽  
Error Matrix ◽  
Squared Error ◽  
The Mean ◽  
Almost Unbiased

This paper is concerned with the parameter estimator in linear regression model. To overcome the multicollinearity problem, two new classes of estimators called the almost unbiased ridge-type principal component estimator (AURPCE) and the almost unbiased Liu-type principal component estimator (AULPCE) are proposed, respectively. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a Monte Carlo simulation study is given to illustrate the performance of the proposed estimators.

scholarly journals A New Ridge-Type Estimator for the Gamma Regression Model

Scientifica ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Adewale F. Lukman ◽  
Issam Dawoud ◽  
B. M. Golam Kibria ◽  
Zakariya Y. Algamal ◽  
Benedicta Aladeitan
Keyword(s):  
Biological Activity ◽  
Regression Model ◽  
Mean Squared Error ◽  
Real Life ◽  
Regression Parameter ◽  
Likelihood Estimator ◽  
Response Variable ◽  
Explanatory Variables ◽  
Squared Error ◽  
The Mean

The known linear regression model (LRM) is used mostly for modelling the QSAR relationship between the response variable (biological activity) and one or more physiochemical or structural properties which serve as the explanatory variables mainly when the distribution of the response variable is normal. The gamma regression model is employed often for a skewed dependent variable. The parameters in both models are estimated using the maximum likelihood estimator (MLE). However, the MLE becomes unstable in the presence of multicollinearity for both models. In this study, we propose a new estimator and suggest some biasing parameters to estimate the regression parameter for the gamma regression model when there is multicollinearity. A simulation study and a real-life application were performed for evaluating the estimators' performance via the mean squared error criterion. The results from simulation and the real-life application revealed that the proposed gamma estimator produced lower MSE values than other considered estimators.

scholarly journals Risk Comparison of Improved Estimators in a Linear Regression Model with MultivariatetErrors under Balanced Loss Function

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Guikai Hu ◽  
Qingguo Li ◽  
Shenghua Yu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Loss Function ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Balanced Loss Function ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Improved Estimators

Under a balanced loss function, we derive the explicit formulae of the risk of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the feasible minimum mean squared error (FMMSE) estimator, and the adjusted feasible minimum mean squared error (AFMMSE) estimator in a linear regression model with multivariateterrors. The results show that the PSR estimator dominates the SR estimator under the balanced loss and multivariateterrors. Also, our numerical results show that these estimators dominate the ordinary least squares (OLS) estimator when the weight of precision of estimation is larger than about half, and vice versa. Furthermore, the AFMMSE estimator dominates the PSR estimator in certain occasions.

scholarly journals A new estimator for the multicollinear Poisson regression model: simulation and application

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adewale F. Lukman ◽  
Emmanuel Adewuyi ◽  
Kristofer Månsson ◽  
B. M. Golam Kibria
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Mean Squared Error ◽  
Model Simulation ◽  
Real Life ◽  
Poisson Regression Model ◽  
Squared Error ◽  
Damage Data ◽  
The Mean ◽  
Better Than

AbstractThe maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). In this study, we propose a new estimator with some biasing parameters to estimate the regression coefficients for the PRM when there is multicollinearity problem. Some simulation experiments are conducted to compare the estimators' performance by using the mean squared error (MSE) criterion. For illustration purposes, aircraft damage data has been analyzed. The simulation results and the real-life application evidenced that the proposed estimator performs better than the rest of the estimators.

scholarly journals Predicting True Value of Used Car using Multiple Linear Regression Model

2020 ◽  
Vol 8 (5S) ◽  
pp. 42-45
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Machine Learning Algorithms ◽  
Price Forecast ◽  
True Value ◽  
Used Car ◽  
Used Cars

Predicting the true value of used cars requires lot of analysis. This prediction takes into account variables such as car model, fuel type, number of owner and so on. In this paper we are applying machine learning algorithms to determine the true value of cars when selling them to the dealers. We have used multiple linear regression model by dividing the data into training and test. Vehicle price forecast is both a critical and significant job, particularly when the car is used and does not come directly from the factory.

Used Car Price Estimation: Moving from Linear Regression towards a New S-Curve Model

2021 ◽  
Vol 22 (3) ◽  
pp. 1174-1187
Author(s):  
Fadzilah Salim ◽  
Nur Azman Abu
Keyword(s):  
Linear Regression ◽  
Linear Model ◽  
Real Life ◽  
Real Data ◽  
Prediction Errors ◽  
Curve Model ◽  
Non Linear ◽  
Used Car ◽  
S Curve ◽  
Used Cars

A simple linear regression is commonly used as a practical predictive model on a used car price. It is a useful model which carry smaller prediction errors around its central mean. Practically, real data will hardly produce a linear relationship. A non-linear model has been observed to better forecast any price appreciation and manage prediction errors in real-life phenomena. In this paper, an S-curve model shall be proposed as an alternative non-linear model in estimating the price of used cars. A dynamic S-shaped Membership Function (SMF) is used as a basis to build an S-curve pricing model in this research study. Real used car price data has been collected from a popular website. Comparisons against linear regression and cubic regression are made. An S-curve model has produced smaller error than linear regression while its residual is closer to a cubic regression. Overall, an S-curve model is anticipated to provide a better and more practical estimate on used car prices in Malaysia.

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