BS3 - Exponentiated Odd Lindley-X Power Series Class of Distributions: Properties and Applications
SCURS Disciplines
Mathematics
Document Type
General Presentation (Oral)
Invited Presentation Choice
Not Applicable
Abstract
Compounding and generalizing probability distributions is an old practice, but it has become more popular recently. This is because flexible models are needed to better fit different kinds of data and characterize their probabilistic structures accurately. In this work, we introduce a new family of probability distributions, the Exponentiated Odd Lindley-X Power Series class, which is derived by integrating the power series distribution with the exponentiated Odd Lindley-X family. As a special case of the generalized model, we propose a new class of distributions called the exponentiated odd Lindley-X power series (EOL-XPS) family of distributions. We derive expressions for various statistical properties, including the quantile function, moments, moment generating function, mean deviation, hazard rate function, order statistics, and entropy.
As a special case, we consider the exponentiated odd Lindley-Weibull Poisson (EOLWP) distribution and conduct a simulation study to assess the validity of the model parameters. We demonstrate the usefulness and versatility of the proposed model by applying it to a COVID-19 dataset from Canada and failure time data (in hours) of Kevlar 49/epoxy strands under 90% pressure. Using these real data sets, model performances was assessed using various goodness-of-fit statistics which include; -2log-likelihood statistics, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Consistent Akaike Information Criterion, Cramér-von Mises, Anderson-Darling, Kolmogorov-Smirnov statistic, as well as its associated p-value. The Exponentiated Odd Lindley-X Power Series (EOL-XPS) class of distributions provides a flexible framework with promising future applications and developments. It can be extended to handle multivariate datasets, making it suitable for complex domains such as finance, engineering, and environmental sciences. Integrating machine learning techniques with the model could enhance predictive accuracy in high-dimensional or time-series data.
Keywords
Probability, Distribution, Parameter Estimation, Exponentiated Odd Lindley-Weibull Poisson
Start Date
10-4-2026 2:40 PM
Location
CASB 102
End Date
10-4-2026 2:55 PM
BS3 - Exponentiated Odd Lindley-X Power Series Class of Distributions: Properties and Applications
CASB 102
Compounding and generalizing probability distributions is an old practice, but it has become more popular recently. This is because flexible models are needed to better fit different kinds of data and characterize their probabilistic structures accurately. In this work, we introduce a new family of probability distributions, the Exponentiated Odd Lindley-X Power Series class, which is derived by integrating the power series distribution with the exponentiated Odd Lindley-X family. As a special case of the generalized model, we propose a new class of distributions called the exponentiated odd Lindley-X power series (EOL-XPS) family of distributions. We derive expressions for various statistical properties, including the quantile function, moments, moment generating function, mean deviation, hazard rate function, order statistics, and entropy.
As a special case, we consider the exponentiated odd Lindley-Weibull Poisson (EOLWP) distribution and conduct a simulation study to assess the validity of the model parameters. We demonstrate the usefulness and versatility of the proposed model by applying it to a COVID-19 dataset from Canada and failure time data (in hours) of Kevlar 49/epoxy strands under 90% pressure. Using these real data sets, model performances was assessed using various goodness-of-fit statistics which include; -2log-likelihood statistics, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Consistent Akaike Information Criterion, Cramér-von Mises, Anderson-Darling, Kolmogorov-Smirnov statistic, as well as its associated p-value. The Exponentiated Odd Lindley-X Power Series (EOL-XPS) class of distributions provides a flexible framework with promising future applications and developments. It can be extended to handle multivariate datasets, making it suitable for complex domains such as finance, engineering, and environmental sciences. Integrating machine learning techniques with the model could enhance predictive accuracy in high-dimensional or time-series data.