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

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...
Types of Selection01:46

Types of Selection

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...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

You might also read

Related Articles

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

Sort by
Same author

A compact low-power magnetic particle imaging scanner based on a permanent-magnet field-free-line generator with high gradient.

The Review of scientific instruments·2026
Same author

Sinomenine restrains the proliferation and hyperactivation of B lymphocytes partly by inhibiting interferon regulatory factor 5.

Journal of ethnopharmacology·2026
Same author

High-fidelity compressed high-speed imaging for resolving rapid micro-dynamics.

Optics express·2026
Same author

Atomically Regulated Symmetry-Breaking Sulfur-Bridged Dual Iron Sites Catalyst for High-Performance Oxygen Reduction Reaction.

Angewandte Chemie (International ed. in English)·2026
Same author

The efficacy of vitamin D supplementation in the management of childhood asthma: a systematic review and meta-analysis.

Frontiers in nutrition·2026
Same author

A wearable non-invasive sonogenetic pacemaker.

Nature biomedical engineering·2026
Same journal

Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data.

Annals of statistics·2026
Same journal

One-Step Estimation of Differentiable Hilbert-Valued Parameters.

Annals of statistics·2026
Same journal

GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING.

Annals of statistics·2026
Same journal

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026
Same journal

TESTING HIGH-DIMENSIONAL REGRESSION COEFFICIENTS IN LINEAR MODELS.

Annals of statistics·2026
Same journal

COUNTERFACTUAL INFERENCE IN SEQUENTIAL EXPERIMENTS.

Annals of statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 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

Variable Selection using MM Algorithms.

David R Hunter1, Runze Li

  • 1Department of Statistics, The Pennsylvania State University University Park, Pennsylvania 16802-2111,

Annals of Statistics
|May 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces novel algorithms for variable selection in high-dimensional statistics. The new minorize-maximize (MM) approach optimizes penalized likelihood functions, ensuring convergence to reliable solutions for complex models.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 23, 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

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

Area of Science:

  • Statistics
  • Machine Learning
  • Computational Statistics

Background:

  • Variable selection is crucial for high-dimensional statistical modeling.
  • Existing methods often rely on optimizing penalized likelihood functions.
  • These functions can be computationally challenging due to non-differentiability or non-concavity.

Purpose of the Study:

  • To propose a new class of algorithms for maximizing penalized likelihood functions.
  • To address challenges posed by non-differentiable and non-concave penalty functions.
  • To develop a robust method for variable selection in complex statistical models.

Main Methods:

  • A novel algorithm class is introduced for penalized likelihood optimization.
  • The method perturbs the penalty function to achieve differentiability.
  • A minorize-maximize (MM) algorithm is employed for optimization, extending EM algorithms.

Main Results:

  • The proposed MM algorithms converge to desirable points.
  • Conditions for guaranteed convergence are discussed.
  • A sandwich estimator for standard errors is developed, leveraging the algorithm's properties.

Conclusions:

  • The new algorithms effectively handle challenging penalized likelihood functions.
  • The method demonstrates good performance in numerical tests.
  • This approach offers a robust solution for variable selection in high-dimensional statistics.