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

Types of Selection01:46

Types of Selection

41.4K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
41.4K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

586
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...
586
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

201
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
201
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

277
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
277
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

382
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
382
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.6K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Responsiveness and minimal clinically important changes of surface topography parameters in adolescents with idiopathic scoliosis: results from the schroth exercise trial.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026
Same author

Bridging the evidence gap in trial inclusion with real world safety profile assessment in relapsed/refractory multiple myeloma patients.

BMC cancer·2026
Same author

Environmental Scan of the United States Regulatory Practices of Physical Therapy Dry Needling.

Physiotherapy research international : the journal for researchers and clinicians in physical therapy·2026
Same author

Elucidating the use of rhinoceros teeth by Neanderthals: Between experiments and the fossil record.

Journal of human evolution·2026
Same author

Aliquot size and sample heterogeneity in environmental studies: consequences for isotope-based interpretation.

The Science of the total environment·2026
Same author

Caprine dairy exploitation on the Iranian Plateau from the seventh millennium BC.

Nature human behaviour·2026

Related Experiment Video

Updated: Sep 6, 2025

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

7.6K

Review of Bayesian selection methods for categorical predictors using JAGS.

Rana Jreich1, Christine Hatte1, Eric Parent2

  • 1Laboratoire des Sciences du climat et de l'environnement LSCE/IPSL, UMR 8212 CEA-CNRS-UVSQ, Paris Scaly University, Gif-sur-Yvette, France.

Journal of Applied Statistics
|June 27, 2022
PubMed
Summary

This study reviews three Bayesian variable selection methods for categorical predictors, offering detailed implementation guidance using JAGS software. These methods enhance model selection for complex data structures.

Keywords:
Bayesian selection methodsJAGScategorical predictorsfusion regression effectssparsityspike and slab priors

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

588

Related Experiment Videos

Last Updated: Sep 6, 2025

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

7.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

588

Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Bayesian variable selection is crucial for model interpretability and performance.
  • Standard methods often struggle with categorical predictors, requiring joint handling of variable levels.
  • New strategies are needed to address grouped effects of categorical covariates.

Purpose of the Study:

  • To review and detail the implementation of three Bayesian variable selection methods for categorical predictors.
  • To provide practical guidance using the JAGS software for these advanced techniques.
  • To assess the performance and hyperparameter sensitivity of these methods in simulated scenarios.

Main Methods:

  • Bayesian Group Lasso with Spike and Slab priors.
  • Bayesian Sparse Group Selection.
  • Bayesian Effect Fusion using model-based clustering.
  • Implementation detailed using JAGS software.

Main Results:

  • The study provides a comprehensive review and implementation guide for three Bayesian selection methods.
  • Performance and sensitivity analyses were conducted under various simulated conditions.
  • JAGS facilitates the application of these methods to complex hierarchical models.

Conclusions:

  • The reviewed Bayesian methods offer robust variable selection for categorical predictors.
  • JAGS software enables practical implementation, even for complex model structures.
  • The findings support the use of these advanced Bayesian techniques in statistical modeling.