DISCRETE KIES DISTRIBUTION : AN ALTERNATIVE TO BINOMIAL DISTRIBUTION FOR COUNT DATA

Abstract

This study presents a discrete form of the Kies distribution, analyzing its distributional properties and reliability metrics. The discrete Kies distribution is characterized by a rising hazard rate function, making it suitable for applications across engineering, biology, and other scientific fields. Various estimation methods, such as maximum likelihood and the method of moments, are utilized to determine the parameters of the model. I conduct extensive simulations to evaluate the effectiveness of these methods, offering quantitative assessments. Moreover, the practical applicability of the model is illustrated through the analysis of a real-world dataset. This research significantly broadens the array of discrete distributions designed for scenarios with increasing hazard rates, contributing to advancements in the areas of reliability and survival data analysis.