Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

273
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
273
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.0K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.0K
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.1K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
12.1K
Probability Distributions01:32

Probability Distributions

12.3K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
12.3K
Sampling Distribution01:12

Sampling Distribution

18.3K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
18.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Generalized Marshall-Olkin exponentiated exponential distribution: Properties and applications.

PloS one·2023
See all related articles

Related Experiment Video

Updated: Feb 19, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K

Modeling physics data with the generalized Marshall-Olkin Kumaraswamy distribution.

Selim Gündüz1, Egemen Ozkan2, Kadir Karakaya3

  • 1Department of Business Administration, Faculty of Business, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.

Plos One
|February 17, 2026
PubMed
Summary

A new statistical distribution for bounded data is introduced, offering flexible modeling for various hazard rates. Its parameters are estimated using multiple methods, showing strong performance in real-world applications across medicine, politics, physics, and education.

More Related Videos

Assembly and Characterization of Polyelectrolyte Complex Micelles
08:44

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

11.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Related Experiment Videos

Last Updated: Feb 19, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K
Assembly and Characterization of Polyelectrolyte Complex Micelles
08:44

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

11.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Area of Science:

  • Statistics
  • Probability Distributions
  • Mathematical Modeling

Background:

  • Traditional statistical models often struggle with bounded data.
  • There is a need for flexible distributions to capture diverse hazard rate shapes.
  • Existing distributions like Beta and Kumaraswamy may not always be optimal.

Purpose of the Study:

  • Introduce a novel statistical distribution defined on a bounded interval.
  • Examine the properties of the new distribution, including moments and associated curves.
  • Develop and evaluate parameter estimation techniques and a quantile regression model.

Main Methods:

  • Introduced a new bounded probability distribution.
  • Investigated moments, Lorenz curves, and Bonferroni curves.
  • Employed maximum likelihood, least squares, Anderson-Darling, Cramér-von Mises, and spacing methods for parameter estimation.
  • Conducted Monte Carlo simulations to assess estimation performance.
  • Developed a quantile regression model for bounded dependent variables.

Main Results:

  • The proposed distribution effectively models various hazard rate shapes (e.g., inverted-bathtub, bathtub, increasing, decreasing).
  • Parameter estimation methods were evaluated, with simulations guiding performance assessment.
  • The new distribution demonstrated applicability and flexibility in real-world data from medicine, politics, physics, and education.
  • Outperformed Beta and Kumaraswamy distributions in specific bounded data modeling scenarios.

Conclusions:

  • The novel distribution provides a robust and flexible tool for analyzing bounded data.
  • The developed quantile regression model enhances capabilities for modeling bounded dependent variables.
  • The distribution is a viable alternative to existing models, with broad applicability in scientific and educational fields.