scholarly journals Contributions to Theil-Sen Regression Analysis Parameter Estimation with Weighted Median

2021 ◽  
Author(s):  
Cem ÖZTAŞ ◽  
Necati Alp ERİLLİ
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Weighted Median

Parameter Estimation in Marketing Models in the Presence of Influential Response Data: Robust Regression and Applications

1984 ◽  
Vol 21 (3) ◽  
pp. 268-277 ◽  
Author(s):  
Vijay Mahajan ◽  
Subhash Sharma ◽  
Yoram Wind
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Parameter Estimates ◽  
Response Data ◽  
Marketing Models ◽  
Marketing Data

In marketing models, the presence of aberrant response values or outliers in data can distort the parameter estimates or regression coefficients obtained by means of ordinary least squares. The authors demonstrate the potential usefulness of the robust regression analysis in treating influential response values in marketing data.

Parameter Estimation: Some Fundamentals of Regression Analysis

2002 ◽  
pp. 215-281
Author(s):  
Claudio Cobelli ◽  
David Foster ◽  
Gianna Toffolo
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis

scholarly journals REGRESI KUANTIL MEDIAN UNTUK MENGATASI HETEROSKEDASTISITAS PADA ANALISIS REGRESI

2013 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
IDA AYU PRASETYA UTHAMI ◽  
I KOMANG GDE SUKARSA ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Quantile Regression ◽  
Secondary Data ◽  
Absolute Error ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
A Value ◽  
Regression Parameters ◽  
Regression Techniques

In regression analysis, the method used to estimate the parameters is Ordinary Least Squares (OLS). The principle of OLS is to minimize the sum of squares error. If any of the assumptions were not met, the results of the OLS estimates are no longer best, linear, and unbiased estimator (BLUE). One of the assumptions that must be met is the assumption about homoscedasticity, a condition in which the variance of the error is constant (same). Violation of the assumptions about homoscedasticity is referred to heteroscedasticity. When there exists heteroscedas­ticity, other regression techniques are needed, such as median quantile regression which is done by defining the median as a solution to minimize sum of absolute error. This study intended to estimate the regression parameters of the data were known to have heteroscedasticity. The secondary data were taken from the book Basic Econometrics (Gujarati, 2004) and analyzing method were performed by EViews 6. Parameter estimation of the median quantile regression were done by estimating the regression parameters at each quantile ?th, then an estimator was chosen on the median quantile as regression coefficients estimator. The result showed heteroscedasticity problem has been solved with median quantile regression although error still does not follow normal distribution properties with a value of R2 about 71 percent. Therefore it can be concluded that median quantile regression can overcome heteroscedasticity but the data still abnormalities.

Parameter Estimation in Marketing Models in the Presence of Multicollinearity: An Application of Ridge Regression

1977 ◽  
Vol 14 (4) ◽  
pp. 586-591 ◽  
Author(s):  
Vijay Mahajan ◽  
Arun K. Jain ◽  
Michel Bergier
Keyword(s):  
Parameter Estimation ◽  
Regression Analysis ◽  
Least Squares ◽  
Ridge Regression ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Estimation Of Parameters ◽  
Marketing Models ◽  
Marketing Data ◽  
Ridge Regression Analysis

In the presence of multicollinearity in data, the estimation of parameters or regression coefficients in marketing models by means of ordinary least squares may give inflated estimates with a high variance and wrong signs. The authors demonstrate the potential usefulness of the ridge regression analysis to handle multicollinearity in marketing data.

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