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
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
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.2K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.2K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

192
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.
192
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

296
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
296
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

293
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...
293

You might also read

Related Articles

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

Sort by
Same author

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same author

Regression with race-modifiers: towards equity and interpretability.

medRxiv : the preprint server for health sciences·2024
Same author

Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same author

Semiparametric count data regression for self-reported mental health.

Biometrics·2021
Same author

Bayesian variable selection for understanding mixtures in environmental exposures.

Statistics in medicine·2021
Same author

Integer-valued functional data analysis for measles forecasting.

Biometrics·2019
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Sep 6, 2025

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

3.4K

Subset selection for linear mixed models.

Daniel R Kowal1

  • 1Dobelman Family Assistant Professor, Department of Statistics, Rice University, Houston, TX, USA.

Biometrics
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach for selecting important variables in linear mixed models (LMMs) with complex data structures. The method identifies a collection of optimal subsets, improving prediction and estimation accuracy.

Keywords:
Bayesian analysishierarchical modelspredictionregressionvariable selection

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.9K
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

Related Experiment Videos

Last Updated: Sep 6, 2025

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

3.4K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.9K
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

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Linear mixed models (LMMs) are essential for analyzing data with inherent structures like clustering or multilevel dependencies.
  • Selecting relevant covariates in LMMs while respecting data structure is a significant statistical challenge.
  • Existing methods often struggle to provide robust variable selection in the presence of structured dependence.

Purpose of the Study:

  • To develop a novel Bayesian decision analysis framework for covariate subset selection in LMMs.
  • To address the challenge of variable selection in the presence of structured dependence within LMMs.
  • To provide a method that offers improved prediction, estimation, and selection abilities.

Main Methods:

  • A Bayesian decision analysis framework is proposed for subset selection in LMMs.
  • A Mahalanobis loss function is utilized, incorporating the structured dependence of the data.
  • Optimal linear coefficients are derived for given subsets and subsets meeting cardinality constraints.
  • The approach leverages shrinkage/regularization and uncertainty quantification inherent in Bayesian models.

Main Results:

  • The proposed method provides optimal linear coefficients for variable subsets within LMMs.
  • Estimates inherit shrinkage and uncertainty quantification from the Bayesian framework.
  • The strategy favors a collection of near-optimal subsets over a single best subset.
  • Demonstrated excellent prediction, estimation, and selection ability on simulated and real-world data.

Conclusions:

  • The Bayesian decision analysis offers a robust solution for covariate selection in LMMs with structured dependence.
  • The focus on a collection of subsets enhances the stability and information content of the selection process.
  • The developed algorithms ensure scalability for practical applications.
  • This approach advances the analysis of complex, structured datasets.