Abstract
Statistical Inferences for Functions of Parameters of Several Pareto and Exponential Populations with Applications in Data Traffic by Sumith Gunasekera Dr. Malwane M. A. Ananda, Examination Committee Chair Professor of Statistics University of Nevada, Las Vegas In this dissertation, we discuss the usability and applicability of three statistical inferential frameworks — namely, the Classical Method, which is sometimes referred to as the Conventional or the Frequentist Method, based on the approximate large sample approach, the Generalized Variable Method based on the exact generalized p-value approach, and the Bayesian Method based on prior densities — for solving existing problems in the area of parametric estimation. These inference procedures are discussed through Pareto and exponential distributions that are widely used to model positive random variables relevant to social, scientific, actuarial, insurance, finance, investments, banking, and many other types of observable phenomena. Furthermore, several Pareto and exponential populations, and the combination of several Pareto and exponential distributions are widely used in the Computer Networking and Data Transmission to model Self-Similar (SS) or Lthe ong-Range-Dependent (LRD) network traffic that can be generated by multiplexing several Pareto and exponentially
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