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A pinboard by
TAYO OGUNDUNMADE

I am A research Assistant at the Laboratory for interdisciplinary Statistical Analysis (LISA), Department of Statistics, University of Ibadan, Nigeria.

I like solving problems and help people in research.

PINBOARD SUMMARY

Estimating parameters of a linear regression with correlated explanatory variables using Bayesian.

This paper addressed the popular issue of collinearity among explanatory variables in the context of a multiple linear regression analysis, and the parameter estimations of both the classical and the Bayesian approaches were compared using Mean Square Error (MSE) and Absolute bias. Five sample sizes: 10, 25, 50, 100 and 500 each replicated 1000 times were simulated using Monte Carlo method. Four degrees of correlation: no correlation, weak correlation, moderate correlation and strong correlation were considered. The estimation techniques considered were, Ordinary Least Squares (OLS); Feasible Generalized Least Squares (FGLS) and Bayesian Methods. The performances of the estimators were evaluated using Absolute Bias (ABIAS) and Mean Square Error (MSE) of the estimates respectively. In all cases considered, the Bayesian estimators performed better. It was consistently more efficient than the OLS and FGLS.

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