scholarly journals Predictive efficiency of ridge regression estimator

2017 ◽  
Vol 27 (2) ◽  
pp. 243-247
Author(s):  
Manoj Tiwari ◽  
Amit Sharma
Keyword(s):  
Least Squares ◽  
Ridge Regression ◽  
Ordinary Least Squares ◽  
Regression Estimator ◽  
Performance Properties ◽  
Regression Estimators ◽  
Ridge Regression Estimator ◽  
Predictive Efficiency

In this article we have considered the problem of prediction within and outside the sample for actual and average values of the study variables in case of ordinary least squares and ridge regression estimators. Finally, the performance properties of the estimators are analyzed.

scholarly journals Modified Ridge Regression Estimator with the Application of Peanut Production in Pakistan

2019 ◽  
pp. 1-8
Author(s):  
Asifa Mubeen ◽  
Nasir Jamal ◽  
Muhammad Hanif ◽  
Usman Shahzad
Keyword(s):  
Ridge Regression ◽  
Real Data ◽  
Production Data ◽  
Regression Estimator ◽  
Mean Square ◽  
Data Set ◽  
Regression Estimators ◽  
Peanut Production ◽  
Ridge Regression Estimator ◽  
The Mean

The main objective of the present study was to develop a new ridge regression estimator and fit the ridge regression model to the peanut production data of Pakistan. Peanut production data has been used to analyze the results. The data has been taken peanut production and growth rate of Pakistan. The mean square error of the proposed estimator is compared with some existing ridge regression estimators. In this study, we proposed a ridge regression estimator. The properties of proposed estimators are also discussed. The real data set of peanut production is used for assuming the performance of proposed and existing estimators. Numerical results of real data set show that proposed ridge regression estimator provides best results as compare to reviewed ones.

Prediction of voluntary Intake of grass silage by growing cattle using ridge regression

1989 ◽  
Vol 1989 ◽  
pp. 68-68 ◽  
Author(s):  
A. J. Rook ◽  
M. Gill ◽  
M. S. Dhanoa
Keyword(s):  
Least Squares ◽  
Ridge Regression ◽  
Prediction Models ◽  
Ordinary Least Squares ◽  
Independent Data ◽  
College Of Agriculture ◽  
Grass Silage ◽  
Backward Elimination ◽  
Growing Cattle ◽  
Controlled Experimentation

Due to collinearity among the independent varlates, intake prediction models based on least squares multiple regression are likely to predict poorly with independent data. In addition, the regression coefficients are sensitive to small changes in the estimation data and tend not to reflect causal relationships expected from the results of controlled experimentation. Ridge regression (Hoerl and Kennard, 1970) allows the estimation of new coefficients for the independent variables which overcome these effects of collinearity. In order to assess the usefulness of the method for Intake prediction, ordinary least squares (OLS) models, obtained using backward elimination of variables, and ridge regression models were constructed from the same data and then tested with independent data.Estimation data consisted of results of experiments of IGAP, Hurley and Greenmount College of Agriculture in which growing cattle were individually fed grass silage ad-libitum with or without supplementary feeds. Two subsets of the estimation data were used. Subset A included 395 animals and 36 silages; subset B included 192 animals and 16 silages and was for Hurley data only.

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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