Advancing Continuous Distribution Generation: An Exponentiated Odds Ratio Generator Approach

TL;DR

A new methodology for generating continuous statistical distributions is presented, integrating the exponentiated odds ratio within the framework of survival analysis, enhancing the flexibility and adaptability of distribution models to effectively address the complexities inherent in contemporary datasets.

Abstract

This paper presents a new methodology for generating continuous statistical distributions, integrating the exponentiated odds ratio within the framework of survival analysis. This new method enhances the flexibility and adaptability of distribution models to effectively address the complexities inherent in contemporary datasets. The core of this advancement is illustrated by introducing a particular subfamily, the "Type-2 Gumbel Weibull-G Family of Distributions." We provide a comprehensive analysis of the mathematical properties of these distributions, encompassing statistical properties such as density functions, moments, hazard rate and quantile functions, Rényi entropy, order statistics, and the concept of stochastic ordering. To establish the robustness of our approach, we apply five distinct methods for parameter estimation. The practical applicability of the Type-2 Gumbel Weibull-G distributions is further supported through the analysis of three real-world datasets. These empirical applications illustrate the exceptional statistical precision of our distributions compared to existing models, thereby reinforcing their significant value in both theoretical and practical statistical applications.

Figures (10)

• Figure 1: MSE of parameters in Table 3
• Figure 2: Left: The pdf of T2GWE distribution for different parameters. Right: The hrf of T2GWE for different parameter values.
• Figure 3: Left: pdf of T2GWU distribution for different values of parameters $\alpha$, $\beta$, and $\gamma$. Right: hrf of T2GWU for selected parameters $\alpha$, $\beta$, and $\gamma$.
• Figure 4: Left: The pdf of T2GWP distribution for selected values of $\alpha$, $\beta$, $\theta$ and $k$. Right: The hrf of T2GWP for various $\alpha$, $\beta$, $\theta$ and $k$.
• Figure 5: left) Fitted density superposed on the histogram and observed probability for the Chemotherapy data. right) Expected probability plots for the Chemotherapy data.