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

Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.0K
Systematic Sampling Method01:17

Systematic Sampling Method

10.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
10.2K
Sampling Distribution01:12

Sampling Distribution

12.4K
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...
12.4K
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.0K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180

You might also read

Related Articles

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

Sort by
Same author

Revealing hidden nonlinear soliton dynamics, multistable regimes, and chaotic transitions in regularized long-wave equations under complex external forcing.

Scientific reports·2026
Same author

Hesperidin as an Emerging Nutraceutical in Modern Health and Preventive Medicine: A Narrative Review.

The Journal of nutrition·2026
Same author

A one-parameter poisson-emrem model for count data: Theory and applications to cytogenetic and COVID-19 studies.

Scientific reports·2026
Same author

Analysis of stability and chaotic trajectories in nonlinear fluid wave interactions under forcing effects.

Scientific reports·2026
Same author

A novel mathematical modeling and optimal control analysis of monkeypox transmission incorporating double-dose vaccination strategies: Insights from the recent outbreak.

Infectious Disease Modelling·2026
Same author

Computational insight to optimize nanofluidic thermal transport management in octagonal enclosure with heat generating element by envisioning novel physical factors.

Discover nano·2026

Related Experiment Video

Updated: Jun 26, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

A statistical framework for a new Kavya-Manoharan Bilal distribution using ranked set sampling and simple random

Anum Shafiq1,2, Tabassum Naz Sindhu3, Muhammad Bilal Riaz2,4

  • 1School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China.

Heliyon
|May 20, 2024
PubMed
Summary

This study introduces a new parsimonious survival model using the Bilal distribution and Kavya-Manoharan transformation. It analyzes theoretical properties and practical parameter estimation for improved survival and lifespan modeling.

Keywords:
KM transformationRanked set samplingSimulationStatistical modelSurvival function

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.9K

Related Experiment Videos

Last Updated: Jun 26, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.9K

Area of Science:

  • Statistics
  • Probability Theory
  • Survival Analysis

Background:

  • Existing survival models often lack theoretical justification or are overly complex.
  • There is a need for parsimonious and theoretically sound survival distributions.

Purpose of the Study:

  • To develop a novel parsimonious survival model by integrating the Bilal distribution with the Kavya-Manoharan transformation.
  • To analyze the theoretical properties, including probability density function (PDF) and hazard rate behavior.
  • To practically assess parameter estimation techniques for the proposed model.

Main Methods:

  • Development of a new survival model based on the Bilal distribution and Kavya-Manoharan transformation.
  • Analytical derivation of single and product moments of order statistics.
  • Parameter estimation using Maximum Likelihood (ML) with Simple Random Sampling (SRS) and Ranked Set Sampling (RSS).
  • Numerical simulations to compare sampling techniques.

Main Results:

  • The proposed Kavya-Manoharan Bilal Distribution offers a parsimonious approach to survival modeling.
  • Explicit equations for moments of order statistics were derived.
  • Maximum Likelihood estimation was successfully applied using both SRS and RSS.

Conclusions:

  • The novel parsimonious survival model provides a theoretically sound and practically applicable alternative.
  • The study demonstrates the utility of integrating existing distributions with transformation families.
  • Comparative analysis of sampling techniques provides insights for efficient parameter estimation in survival analysis.