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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

79
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
79
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

220
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...
220
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

103
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
103
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

469
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...
469
Study Design in Statistics01:15

Study Design in Statistics

8.2K
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...
8.2K
Survival Tree01:19

Survival Tree

105
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
105

You might also read

Related Articles

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

Sort by
Same author

Research on SAR Image Target Recognition Method Based on Multi-Dimensional Feature Fusion.

Sensors (Basel, Switzerland)·2026
Same author

Synergistic polyphosphate-gallic acid system heals infected skin defects by remodeling the immune microenvironment and restoring bioenergetics.

Journal of materials chemistry. B·2026
Same author

An implantable mechano-electro cascade platform synchronizes neuro-muscle repair.

Nature communications·2026
Same author

Hypervirulent <i>Klebsiella pneumoniae</i> induces liver abscess by promoting neutrophil extracellular trap formation through NLRP3 inflammasome activation.

Microbiology spectrum·2026
Same author

Yam-Active Protein Protects Against Cyclophosphamide-Induced Testicular Injury by Suppressing Inflammatory Responses.

Molecules (Basel, Switzerland)·2026
Same author

Reprogramming of nitrogen metabolism in tumors: mechanisms and therapeutic implications.

Amino acids·2026
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
See all related articles

Related Experiment Video

Updated: Jul 16, 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

7.5K

A comparative study of different variable selection methods based on numerical simulation and empirical analysis.

Dake Hou1, Wenli Zhou2, Qiuxia Zhang3

  • 1School of Mathematics, Shandong University, Jinan, China.

Peerj. Computer Science
|September 14, 2023
PubMed
Summary
This summary is machine-generated.

This study evaluates Lasso variable selection methods for linear random effect models. The proposed evaluation method effectively assesses model consistency, prediction accuracy, stability, and efficiency.

Keywords:
BoxplotCoefficient consistencyLinear random effect modelPrediction accuracyStabilityVariable 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

4.6K
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

2.1K

Related Experiment Videos

Last Updated: Jul 16, 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

7.5K
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

4.6K
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

2.1K

Area of Science:

  • Computer Science
  • Statistics
  • Statistical Modeling

Background:

  • Linear random effect models are widely used.
  • Variable selection is crucial for model performance.
  • Existing methods for evaluating model efficacy have limitations.

Purpose of the Study:

  • To evaluate the efficacy of the linear random effect model with Lasso variable selection techniques.
  • To introduce a novel approach for assessing variable selection consistency.
  • To compare the prediction accuracy, stability, and efficiency of different Lasso methods.

Main Methods:

  • Numerical simulation and empirical research were employed.
  • Lasso, Elastic-Net, Adaptive-Lasso, and SCAD techniques were utilized.
  • A novel consistency measure using the angle between coefficient vectors was developed.
  • Boxplots were used to visualize prediction accuracy and consistency.
  • Comparative experiments evaluated a proposed model evaluation method.

Main Results:

  • The proposed model evaluation method demonstrated effectiveness and correctness.
  • The study provides insights into the consistency, prediction accuracy, stability, and efficiency of Lasso methods.
  • The novel consistency measure proved useful in comparative analysis.
  • Boxplots effectively represented data distributions.

Conclusions:

  • The proposed method offers a convenient way to analyze the stability and efficiency of fitting models.
  • Lasso variable selection techniques show varying performance in linear random effect models.
  • Further research can build upon the novel evaluation approach for enhanced model selection.