Search

Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. Usually, clustering methods work based on some distance metric among the observation or it may use Co-variance and correlation structure among the variables. If all the given variables depend on a single variable, then the procedure of clustering the observations is said to be regression clustering. In this paper, an iterative procedure of regression clustering method was proposed by using the famous Cook's D distance. At first, the Cook's D distance should be calculated for the entire sample, then fix a Cut-off distance proposed by (Bollen and Jackman, 1990) as 4=(n - K - 1). The authors fixed this Cut-off point as structural break in the sample, observations above the cut-off are considered as In uential which are grouped as In uential cluster and repeat the same procedure for the remaining observations, until there are no influential observations in the last cluster. At each iteration, Chow's F-test (1960) was used to check the discrimination between the in uential cluster and the non-influential cluster. Moreover, control charts also used to graphically visualizes the iterations and the clustering process. Finally Chow's test of equality of several regression equation helps firmly to establish the cluster discrimination and validity. This paper employed this procedure for clustering 220 customers of a famous four-wheeler in India based on 19 different attributes of the four wheeler and its company. [ABSTRACT FROM AUTHOR]

Multicollinearity is the problem experienced by statisticians while evaluating a sample regression model. This paper explored the relationship between the sample variance in ation factor (vif) and F-ratio, based on this we proposed an exact F-test for the identification of multicollinearity and it overcomes the traditional procedures of rule of thumb. The authors critically identified that the variance in ation factor not only in ates the variance of the estimated regression coefficient and it also in ates the residual error variance of a given fitted regression model in various level of in ation. Moreover, we also found a link between the problem of multicollinearity and its impact on the model selection decision. For this, the authors proposed multicollinearity corrected version of generalized information criteria which incorporates the effeect of multicollinearity and help the statisticians to select a best model among the various competing models. This procedure numerically illustrated by fitting 12 different types of stepwise regression models based on 44 independent variables in a BSQ (Bank service Quality) study. Finally.the study result shows the transition in model selection after the correction of multicollinearity effect. [ABSTRACT FROM AUTHOR]