Odd Generalized Exponential Kumaraswamy–Weibull Distribution

A novel odd generalized exponential Kumaraswamy–Weibull distribution is defined. This distribution is distinguished by its capacity to capture a wider class of hazard functions than the standard Weibull models, such as non-monotonic and bathtub-shaped hazards. This is an advancement in distribution theory because it provides a new simplified form of the distribution with a much more complicated behavior, which results in better statistical inference and detail in survival analysis and other related fields. Considerations on the identifiability of the proposed distribution are addressed, emphasizing the distinct contributions of its parameters and their roles in model behavior characterization. One real dataset from a survival experiment is considered, highlighting the practical implications of our distribution in the context of reliability.

Authors

Institutions

Publication Details

Journal
Mathematics
Published
2025-03-30
DOI
https://doi.org/10.3390/math13071136
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.63
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Odd Generalized Exponential Kumaraswamy–Weibull Distribution

Sandra S. Ferreira, Dário Ferreira
1 citations
Mathematics
Statistical Distribution Estimation and Applications
2.63
article

Odd Generalized Exponential Kumaraswamy–Weibull Distribution

Sandra S. Ferreira, Dário Ferreira
article en
1 citations

Abstract

A novel odd generalized exponential Kumaraswamy–Weibull distribution is defined. This distribution is distinguished by its capacity to capture a wider class of hazard functions than the standard Weibull models, such as non-monotonic and bathtub-shaped hazards. This is an advancement in distribution theory because it provides a new simplified form of the distribution with a much more complicated behavior, which results in better statistical inference and detail in survival analysis and other related fields. Considerations on the identifiability of the proposed distribution are addressed, emphasizing the distinct contributions of its parameters and their roles in model behavior characterization. One real dataset from a survival experiment is considered, highlighting the practical implications of our distribution in the context of reliability.

MathematicsVol. 13(7)
University of Beira Interior (PT)
Openalex Percentile: Top 13%
Statistical Distribution Estimation and Applications
2.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.