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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.8K
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...
4.8K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

834
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
834
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

174
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...
174
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

387
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
387
Study Design in Statistics01:15

Study Design in Statistics

9.8K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
9.8K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

261
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
261

You might also read

Related Articles

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

Sort by
Same author

Non-Linear Association Between Phase Angle and Body Fat in a Sample of US Adults.

Biology·2025
Same author

A bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance.

Journal of applied statistics·2025
Same author

A Bayesian non-parametric modeling to estimate student response to ICT investment.

Journal of applied statistics·2024
Same author

Transfer Learning in Multiple Hypothesis Testing.

Entropy (Basel, Switzerland)·2024
Same author

Model uncertainty quantification in Cox regression.

Biometrics·2023
Same author

P-value calibration in multiple hypotheses testing.

Statistics in medicine·2017
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.9K

A Dirichlet Process Prior Approach for Covariate Selection.

Stefano Cabras1

  • 1Department of Statistics, Universidad Carlos III de Madrid, 28903 Madrid, Spain.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study addresses variable selection for large linear regression models using Gibbs sampling. It introduces a Bayesian non-parametric approach to estimate model inclusion probabilities, improving model selection accuracy.

Keywords:
Dirichlet process priorconventional priorscovariate inclusion probabilitynon-local priorordinary linear regressionvariable selection

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Related Experiment Videos

Last Updated: Nov 27, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Area of Science:

  • Statistics
  • Econometrics

Background:

  • Variable selection is challenging in large ordinary linear regression models where exploring all models is infeasible.
  • Gibbs-sampling is essential for stochastic model exploration and estimating model inclusion probabilities.

Purpose of the Study:

  • To analyze Gibbs-sampling output using a Bayesian non-parametric prior model.
  • To compare conventional and non-local prior approaches for Bayes Factors in model selection.

Main Methods:

  • Utilizing Gibbs-sampling for stochastic model exploration in high-dimensional regression.
  • Applying a Bayesian non-parametric prior model to analyze simulation output.
  • Comparing conventional versus non-local priors for Bayes Factor model selection.

Main Results:

  • The empirical estimator is shown to be an asymptotic version of the expected posterior inclusion probability from Gibbs-sampling.
  • Alternative posterior conditional estimators are related to latent probability distributions on the model space.
  • The study compares conventional and non-local prior approaches for model selection.

Conclusions:

  • The proposed Bayesian non-parametric approach provides a robust method for analyzing Gibbs-sampling output in variable selection.
  • Understanding latent probability distributions enhances model selection strategies.
  • The comparison of prior approaches offers insights into effective Bayes Factor utilization for large model spaces.