Skip to content
Research Article Open access CC BY 3.0

Using the Average of the Extreme Values of a Triangular Distribution for a Transformation, and Its Approximant via the Continuous Uniform Distribution

H. I. Okagbue, S. O. Edeki, A. A. Opanuga, P. E. Oguntunde, M. E. Adeosun

Journal of Advances in Mathematics and Computer Science · pp. 3497–3507 · Published 9 Oct 2014

10.9734/BJMCS/2014/12299

Abstract

This paper introduces a new probability distribution referred to as a transformed triangular distribution (TTD) by using the average of the extreme values (minimum and maximum) of the triangular distribution. The TTD is being approximated by the continuous uniform distribution. The basic moments of the TTD and those of the continuous uniform distribution are compared respectively, and a relationship established. This can be used in modeling and simulation.

Moments uniform distribution triangular distribution transformed distribution continuous random variable.

References (21)

  1. 1 Operations Research: Applications and Algorithms [DOI]
  2. 2 Simulation Modeling and Analysis with ARENA
  3. 3 The triangular distribution as a proxy for the beta distribution in risk analysis [DOI]
  4. 4 Better Estimation of PERT Activity Time Parameters [DOI]
  5. 5 A novel extension of the triangular distribution and its parameter estimation [DOI]
  6. 6 Defining Triangular Probability Distributions from Historical Cost Data [DOI]
  7. 7 Discrete triangular distributions and non-parametric estimation for probability mass function [DOI]
  8. 8 New validations of PERT times [DOI]
  9. 9 Generalizations of Two-Sided Power Distributions and Their Convolution [DOI]
  10. 10 Non-Smooth Sailing or Triangular Distributions Revisited After Some 50 Years [DOI]
  11. 11 Modeling Risk [DOI]
  12. 12 A polynomial model for bivariate extreme value distributions [DOI]
  13. 13 Triangular approximations for continuous random variables in risk analysis [DOI]
  14. 14 PRODUCTS AND QUOTIENTS OF RANDOM VARIABLES AND THEIR APPLICATIONS
  15. 15 EVALUATION OF RESEARCH EXPENDITURES USING TRIANGULAR DISTRIBUTION FUNCTIONS AND MONTE CARLO METHODS [DOI]
  16. 16 ON A BIVARIATE TRIANGULAR DISTRIBUTION1 [DOI]
  17. 17 Min And Max Triangular Extreme Interval Values And Statistics [DOI]
  18. 18 Estimation of HD drilling time using deterministic and triangular distribution functions
  19. 19 A Skewed Model Combining Triangular and Exponential Features: The Two-faced Distribution and its Statistical Properties [DOI]
  20. 20 Letter to the Editor—The PERT Model for the Distribution of an Activity Time [DOI]
  21. 21 Letter to the Editor—Attempts to Validate Certain PERT Statistics or “Picking on PERT” [DOI]

Cited by 6

Non-Poisson queueing model’s identification (Case study: AKAP and AKDP bus on the West Lines bus service of Tirtonadi Surakarta)

A. P. Wrediningsih, Sugito, A. Prahutama · Journal of Physics, Conference Series · 2019

Queue theory for triangular and weibull arrival distribution models (case study of Banyumanik toll)

Sugito, R. Rahmawati, Jenesia Kusuma Wardhani · 2018

A Review of Activity Time Distributions in Risk Analysis

E. F. Udoumoh, D. Ebong · 2017

Showing 3 of 6 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

8

Page views

0

PDF downloads

5

Outbound clicks

6

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.