Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Statistics > Methodology

arXiv:2410.19019 (stat)
[Submitted on 24 Oct 2024 (v1), last revised 27 Nov 2024 (this version, v4)]

Title:Median Based Unit Weibull (MBUW): a new unit distribution Properties

Authors:Iman Mohammed Attia
View PDF
Abstract:A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.
Comments: arXiv admin note: text overlap with arXiv:2410.04132 , this a new update as the new update contains real data anlysis ( 6 data sets ) to illustrate the benefit of the distribution
Subjects: Methodology (stat.ME); Probability (math.PR)
MSC classes: 60E05, 62E15, 62E10, 62F10, 62F03
Cite as: arXiv:2410.19019 [stat.ME]
  (or arXiv:2410.19019v4 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.2410.19019

Submission history

From: Iman Mohammed Attia Abd Elkhalik Abo Elreesh Dr. [view email]
[v1] Thu, 24 Oct 2024 04:26:47 UTC (4,253 KB)
[v2] Mon, 28 Oct 2024 16:51:54 UTC (24,556 KB)
[v3] Tue, 26 Nov 2024 16:55:22 UTC (7,182 KB)
[v4] Wed, 27 Nov 2024 02:13:29 UTC (7,182 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
license icon view license
Current browse context:
stat.ME
< prev   |   next >
new | recent | 2024-10
Change to browse by:
math
math.PR
stat

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)