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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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 observed.

You might also read

Related Articles

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

Sort by
Same author

Single-cell transcriptomics unveils pyroptosis-related immune microenvironment dynamics and prognostic modeling in esophageal squamous cell carcinoma.

Journal of cardiothoracic surgery·2026
Same author

Elucidating the toxicity of the plasticizer DPhP: Evidence of neurotoxicity and developmental impairment from <i>in vitro</i> to <i>in vivo</i> models.

Current research in toxicology·2026
Same author

Linearity enhancement of linear frequency-modulated DFB semiconductor lasers based on smoothing algorithms.

Applied optics·2026
Same author

Jasmonic Acid Gradients Reprogram Rice Transcription: Mild Stress Priming to Growth-Defense Balance to Strong Defense Activation.

Annals of botany·2026
Same author

Combined biochemical and genetic analysis improves early diagnosis and prenatal assessment of multiple acyl-CoA dehydrogenase deficiency.

World journal of pediatrics : WJP·2026
Same author

Damage response and postnatal compensatory repair in the male offspring mouse reproductive system following gestational cadmium exposure.

Toxicology and applied pharmacology·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 19, 2026

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

One-step Sparse Estimates in Nonconcave Penalized Likelihood Models.

Hui Zou1, Runze Li

  • 1University of Minnesota and The Pennsylvania State University.

Annals of Statistics
|October 14, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for variable selection using penalized likelihood with concave penalties. The proposed method efficiently handles nonconcave functions, offering computational advantages and oracle properties for sparse estimation.

Related Experiment Videos

Last Updated: Jun 19, 2026

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

Area of Science:

  • Statistics
  • Machine Learning
  • Computational Statistics

Background:

  • Variable selection is crucial in high-dimensional data analysis.
  • Concave penalty functions offer desirable oracle properties for variable selection.
  • Maximizing nonconcave penalized likelihood functions is computationally challenging due to non-differentiability.

Purpose of the Study:

  • To propose a unified algorithm for maximizing penalized likelihood using concave penalty functions.
  • To address the computational challenges associated with nonconcave penalized likelihood estimators.
  • To develop efficient and statistically sound variable selection methods.

Main Methods:

  • A novel unified algorithm based on local linear approximation (LLA) is proposed.
  • The LLA algorithm is designed to handle a broad class of concave penalty functions.
  • The one-step LLA estimator is suggested for final estimates, leveraging sparse representation.

Main Results:

  • Convergence and theoretical properties of the LLA algorithm are established.
  • The one-step LLA estimates achieve oracle properties when the regularization parameter is chosen appropriately.
  • The LLA algorithm significantly reduces computational cost for nonconcave penalized likelihood maximization.

Conclusions:

  • The proposed LLA algorithm provides an efficient solution for variable selection with concave penalties.
  • The one-step LLA estimator offers a computationally tractable approach with desirable statistical properties.
  • Simulation studies indicate strong finite sample performance for the proposed sparse estimation methods.