Estimation of Parameters of an Extension of Exponential Distribution

This paper aims to the estimation of the parameters for an extension of exponential distribution (EED). We have used different estimation methods namely, method of maximum likelihood estimation (MLE), method of Maximum product spacing (MPS) and method of least square (LSE) to obtain the estimates of the parameters. The performances of the estimators have been studied on the basis of Monte Carlo simulation, and finally, a real data set has been used for the illustrative purpose of the study. overing of previously unknown correlations, patterns and trends from large amounts of data stored in multiple data sources. It is a powerful new technology with great potential to help businesses make full use of the available data for competitive advantages. Data mining application success stories have been told in different areas among them; healthcare, Banking and finance and telecommunication. This survey paper reviews some major mining techniques and key challenges. It also draws attention to useful applications, giving a small collection of real-life examples of data mining implementations from the business to the scientific world.