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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

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

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

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

Regularized parameter estimation in high-dimensional gaussian mixture models.

Lingyan Ruan1, Ming Yuan, Hui Zou

  • 1School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. lruan@gatech.edu

Neural Computation
|March 15, 2011
PubMed
Summary
This summary is machine-generated.

We introduce a penalized likelihood estimator for high-dimensional Gaussian mixture models, reducing complexity by promoting sparsity in inverse covariance matrices. This method offers an efficient solution for complex statistical analyses.

More Related Videos

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the &#181;s-ms Timescale
08:09

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 3, 2026

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

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the &#181;s-ms Timescale
08:09

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Finite Gaussian mixture models (GMMs) are flexible statistical tools.
  • High-dimensional GMMs present parameter estimation challenges due to numerous parameters.

Purpose of the Study:

  • To propose a penalized likelihood estimator for high-dimensional GMMs.
  • To reduce the effective dimensionality of the estimation problem.

Main Methods:

  • Utilizing a [Formula: see text]-type penalty on inverse covariance matrices to induce sparsity.
  • Employing an expectation-maximization algorithm for efficient computation of the proposed estimator.

Main Results:

  • The penalized estimator effectively reduces dimensionality by encouraging sparsity.
  • The method is computationally efficient, demonstrated via an expectation-maximization algorithm.
  • Successful applications shown in model-based clustering and mixture discriminant analysis.

Conclusions:

  • The proposed penalized likelihood estimator is a valuable tool for high-dimensional data analysis.
  • The method addresses the challenges of parameter estimation in complex GMMs.
  • Demonstrated practical utility in clustering and discriminant analysis with simulated and real data.