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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

521
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...
521
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

103
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
103
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

120
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.
120
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

74
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...
74
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

92
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...
92
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K

You might also read

Related Articles

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

Sort by
Same author

Outlier Detection in Functional Data Using Adjusted Outlyingness.

Entropy (Basel, Switzerland)·2026
Same author

Mechanical Properties and Uniaxial Failure Behavior of Concrete with Different Solid Waste Coarse Aggregates.

Materials (Basel, Switzerland)·2022
Same author

The updated role of exosomal proteins in the diagnosis, prognosis, and treatment of cancer.

Experimental & molecular medicine·2022
Same author

Objective Assessment of the Nature and Extent of Children's Internet-Based World: Protocol for the Kids Online Aotearoa Study.

JMIR research protocols·2022
Same author

Trend of hand, foot, and mouth disease from 2010 to 2021 and estimation of the reduction in enterovirus 71 infection after vaccine use in Zhejiang Province, China.

PloS one·2022
Same author

Cross-habitat distribution pattern of Bacillus communities and their capacities of producing industrial hydrolytic enzymes in Paracel Islands: Habitat-dependent differential contributions of the environment.

Journal of environmental management·2022
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Aug 12, 2025

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

4.8K

Component selection for exponential power mixture models.

Xinyi Wang1, Zhenghui Feng1,2

  • 1The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, People's Republic of China.

Journal of Applied Statistics
|January 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a penalized likelihood method for Exponential Power (EP) mixture models, improving component selection and parameter estimation accuracy. The flexible EP family offers advantages over traditional Gaussian models in statistical analysis.

Keywords:
Exponential power mixture modelSHIBORcomponents selectiondensity estimationexponential power mixture regression Model

More Related Videos

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

3.4K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K

Related Experiment Videos

Last Updated: Aug 12, 2025

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

4.8K
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

3.4K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K

Area of Science:

  • Statistics
  • Probability Theory
  • Data Science

Background:

  • The Exponential Power (EP) distribution family offers greater flexibility than the Gaussian family.
  • Existing mixture models often assume zero component means, limiting their applicability.
  • Robust statistical modeling requires flexible distributions and accurate component selection.

Purpose of the Study:

  • To develop a method for simultaneous component selection and parameter estimation in EP mixture models and regressions.
  • To relax the restrictive assumption of zero component means in prior work.
  • To enhance the accuracy of statistical modeling for complex data.

Main Methods:

  • A penalized likelihood approach is proposed to shrink mixing proportions, enabling automatic component selection.
  • Modified Expectation-Maximization (EM) algorithms are developed for parameter estimation.
  • The consistency of the estimated number of components is theoretically established.

Main Results:

  • The penalized likelihood method effectively selects components by shrinking mixing proportions to zero.
  • Modified EM algorithms provide accurate parameter and density estimations.
  • Simulation studies demonstrate superior performance in component number selection and estimation accuracy compared to existing methods.

Conclusions:

  • The proposed penalized likelihood method offers a robust and accurate approach for EP mixture models and regressions.
  • The relaxation of the zero component mean assumption broadens the applicability of these models.
  • The methods are validated through simulations and real-world data analyses, including financial risk and climate change.