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Recent technological advances have led to a flood of new data on cosmologyrich in information about the formation and evolution of the universe, e.g.,the data collected in Sloan Digital Sky Survey (SDSS) for more than 200 millionobjects. The analyses of such data demand cutting edge statisticaltechnologies. Here, we have used the concept of mixture model within Bayesiansemiparametric methodology to fit the regression curve with the bivariate datafor the apparent magnitude and redshift for Quasars in SDSS (2007) catalogue.Associated with the mixture modeling is a highly efficient curve-fittingprocedure, which is central to the application considered in this paper.Moreover, we adopt a new method for analysing the posterior distribution ofclusterings, also generated as a by-product of our methodology. The results ofour analysis of the cosmological data clearly indicate the existence of fourchange points on the regression curve andposssibiltiy of clustering of quasarsspecially at high redshift. This sheds new light not only on the issue ofevolution, existence of acceleration or decceleration and environment aroundquasars at high redshift but also help us to estimate thecosmologicalparameters related to acceleration or decceleration.

Fast Bayesian Semiparametric Curve-Fitting and Clustering in Massive Data With Application to Cosmology