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

407
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...
407
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
335
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

596
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,...
596
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

316
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...
316
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.3K
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...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Identification of novel candidate neural genes for diet-induced obesity in outbred heterogeneous stock rats.

Research square·2026
Same author

Cross-coronavirus host susceptibility loci influence disease severity through immune mediators.

bioRxiv : the preprint server for biology·2026
Same author

Genetic mapping in collaborative cross mouse strains identifies loci that affect initial sensitivity to cocaine.

Psychopharmacology·2025
Same author

The impact of early-life exposures on growth and adult gut microbiome composition is dependent on genetic strain and parent- of- origin.

Microbiome·2025
Same author

Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes.

PLoS genetics·2025
Same author

Sarbecovirus disease susceptibility is conserved across viral and host models.

Virus research·2024

Related Experiment Video

Updated: Apr 6, 2026

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

8.1K

A permutation approach for selecting the penalty parameter in penalized model selection.

Jeremy A Sabourin1,2, William Valdar1,3, Andrew B Nobel4,3,5

  • 1Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.

Biometrics
|August 6, 2015
PubMed
Summary

This study introduces permutation selection, a computationally efficient method for choosing the penalty parameter in LASSO-penalized regression, prioritizing variable selection. It offers a practical alternative to cross-validation and other criteria for generalized linear models.

Keywords:
LASSOPenalized regressionVariable 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

6.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Related Experiment Videos

Last Updated: Apr 6, 2026

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

8.1K
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

6.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Area of Science:

  • Statistics
  • Machine Learning
  • Biomedical Data Analysis

Background:

  • LASSO-penalized regression is widely used for variable selection.
  • Selecting an appropriate penalty parameter is crucial for effective LASSO application.
  • Existing methods like cross-validation (CV) and Bayesian information criterion (BIC) have limitations.

Purpose of the Study:

  • To introduce a novel, computationally efficient permutation-based procedure for selecting the LASSO penalty parameter.
  • To evaluate the performance of this new method, termed permutation selection, against established techniques.
  • To demonstrate the applicability of permutation selection across various statistical models, including generalized linear models.

Main Methods:

  • Development of a permutation-based algorithm for LASSO penalty parameter selection.
  • Comparison of permutation selection with CV, BIC, scaled sparse linear regression, and LASSO testing procedures.
  • Application and evaluation using simulation studies and real biomedical datasets.

Main Results:

  • Permutation selection provides a simple and computationally efficient approach to LASSO parameter tuning.
  • The method demonstrates competitive or superior performance compared to CV, BIC, and other selection criteria in simulation and real-world data.
  • Permutation selection is versatile and applicable to generalized linear models.

Conclusions:

  • Permutation selection is a valuable and efficient tool for variable selection in LASSO-penalized regression.
  • This method offers a robust alternative for researchers focusing on identifying significant predictors in complex datasets.
  • The study validates permutation selection's utility in both simulated and authentic biomedical research settings.