TL;DR
The proposed Log–Exponential Fréchet distribution and the associated inferential framework provide an effective and flexible approach for modeling and analyzing heavy-tailed data in reliability and engineering applications.
Recent advancements in distributional theory have increasingly focused on constructing flexible probability models capable of capturing complex real-world phenomena more effectively. Addressing this methodological need, this study introduces the Fréchet Weibull distribution, a novel extension of the classical Weibull m...
Expectiles have received increasing attention as a market risk measure that is both coherent and elicitable. They are defined as a least squares analog to quantiles. Their estimation from heavy-tailed loss data in an extreme value framework is an important and difficult problem, especially when the target tail expectil...
This paper introduces a new two-parameter log-family of distributions for modeling data defined on the open unit interval (0, 1), such as proportions, rates, and percentage-based lifetime observations. The proposed family has tractable expressions for the density, distribution, survival, hazard, cumulative hazard, reve...
This paper studies the geometric–natural discrete Lindley (GNDL) distribution as a structurally motivated one-parameter submodel of the published two-parameter natural discrete Lindley (TNDL) family, rather than as a new unrestricted family. The model arises from a nested geometric–NDL construction and admits equivalen...
We propose a class of extended generalized Pareto models for right-censored survival data, with particular emphasis on tail inference and long-term extrapolation. Our framework integrates extreme value theory and survival analysis, combining a generalized Pareto distribution with a flexible perturbation distribution on...
The classical two-sample problem is framed as the detection of differences in marginal distributions, in terms of location, scale, or overall shape. In this paper, Gap–Entropy Testing (GET) is proposed, a novel nonparametric framework that extends the two-sample paradigm by adding a spacing-sensitive component to conve...
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