In this paper we construct thirteen different types of composite indices by linear combination of indicator variables (with and without outliers/data corruption). Weights of different indicator variables are obtained by maximization of the sum of squared (and, alternatively, absolute)...

The Two-Stage Least Squares (2-SLS) is a well known econometric technique used to estimate the parameters of a multi-equation (or simultaneous equations) econometric model when errors across the equations are not correlated and the equation(s) concerned is (are) over-identified or exactly...

Social sector with the objective to satisfy the welfare needs of the people and to correct the imbalances in the economy claims a sizeable proportion of the public expenditure and has emerged as a significant sector. This paper in this regard is a state level analysis on the growth of public...

This paper elaborates on 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 Campbell’s...

The Pearsonian coefficient of correlation as a measure of association between two variates is highly prone to the deleterious effects of outlier observations (in data). Statisticians have proposed a number of formulas to obtain robust measures of correlation that are considered to be less...

Effects of outliers on mean, standard deviation and Pearson’s correlation coefficient are well known. The Principal Components analysis uses Pearson’s product moment correlation coefficients to construct composite indices from indicator variables and hence may be very sensitive to effects of...