Extension of burr v distribution: its properties and application to real-life data

Thesis Abstract

Abstract
The Burr V distribution is a versatile probability distribution that has been widely used in various applications due to its flexibility in modeling a wide range of data types. In this research project, we focus on the extension of the Burr V distribution by introducing a new parameter that enhances its properties and applicability to real-life data. We investigate the mathematical properties of the extended Burr V distribution, including moments, generating functions, and order statistics. Furthermore, we explore the application of the extended Burr V distribution to analyze real-life data sets from different fields such as finance, engineering, and environmental science. We compare the performance of the extended Burr V distribution with other commonly used distributions to demonstrate its effectiveness in capturing the underlying characteristics of the data. Our research provides insights into the behavior of the extended Burr V distribution under various scenarios and highlights its potential advantages over existing distributions in terms of flexibility and goodness of fit to empirical data. We also develop estimation methods for the parameters of the extended Burr V distribution using maximum likelihood estimation and explore the robustness of these estimation procedures through simulation studies. Moreover, we conduct a case study using real-life data to illustrate the practical utility of the extended Burr V distribution in modeling extreme events and tail behavior. By fitting the extended Burr V distribution to the data, we are able to make reliable predictions and quantify the risk associated with extreme events, which is crucial for decision-making in risk management and reliability analysis. Overall, this research project contributes to the advancement of statistical theory and methodology by extending the Burr V distribution and enhancing its capabilities for modeling complex data sets. The findings of this study have implications for a wide range of disciplines that deal with uncertainty and variability in data, providing researchers and practitioners with a valuable tool for analyzing and interpreting data in a more accurate and reliable manner.