Estimation of Parameters of Some Distribution Functions and its Application to Optimization Problem

Abstract

The thesis addresses the study of some basic results used in statistics and estimation of parameters. Here we have presented estimation of parameters for some well known
discrete distribution functions. Chapter 1, contains an introduction to estimation theory and motivation. Chapter 2, contains some basic results, definitions, methods of estimation and Chance constraint approach in linear programming problem. In chapter 3, we have estimated the parameters of well known discrete distribution functions by different methods like method of moments, method of maximum likelihood estimation and Bayes estimation. Further in Chapter 4, a Chance Constraint method is discussed which is an application of beta distribution function.