This paper has elaborated upon the deleterious effects of outliers and corruption of dataset on estimation of linear regression coefficients by the Ordinary Least Squares method. Motivated to ameliorate the estimation procedure, we have introduced the robust regression estimators based on...

Johan Gielis showed that all closed curves might be considered as some sort of deformed ellipses. He gave a superformula to parameterize such shapes. In this study an attempt has been made to estimate the parameters of Gielis' superformula from empirical data. We use an optimum search algorithm...

Ricardo Chacoacute;n generalized Johan Gielis's superformula by introducing elliptic functions in place of trigonometric functions. In this paper an attempt has been made to fit the Chacoacute;n-Gielis curves (modified by various functions) to simulated data. Estimation has been done by the...

The Two-Stage Least squares method for obtaining the estimated structural coefficients of a simultaneous linear equations model is a celebrated method that uses OLS at the first stage for estimating the reduced form coefficients and obtaining the expected values in the arrays of current...

Multicollinearity in empirical data violates the assumption of independence among the explanatory variables in a linear regression model and by inflating the standard error of estimates of the estimated regression coefficients leads to failure in rejecting a false null hypothesis of...

In this paper an attempt has been made to estimate the parameters of Gielis superformula (modified by various functions). Simulated data have been used for this purpose. The estimation has been done by the method of simulated annealing. It has been found that the simulated annealing method is...

A high degree of multicollinearity among the explanatory variables severely impairs estimation of regression coefficients by the Ordinary Least Squares. Several methods have been suggested to ameliorate the deleterious effects of multicollinearity. In this paper we aim at comparing the...

A high degree of multicollinearity often has a detrimental effects on the estimation of a linear econometric (regression) model due to an intricate internecine sharing among the estimated regression coefficients (beta). Paris (2001) introduced the Maximum Entropy Leuven (MEL) estimator. It...

Deleterious effects of a high degree of multicollinearity on estimation of regression coefficients (beta) of a linear model are well known. As a remedial measure, Hoerl & Kennard (1970) introduced Ridge Regression, but as Theobald (1974) pointed out, its optimality depends on unknown parameters...