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

Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...
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 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)...
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...
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

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,...
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...

You might also read

Related Articles

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

Sort by
Same author

External Fixation for Ballistic Mandibular Trauma: An Old Method for a Modern Problem.

Annals of plastic surgery·2026
Same author

Routine Screening for Neurocognitive Impairment in Patients with Craniosynostosis: Towards a Standardized Approach.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2025
Same author

Combining experimental and observational data through a power likelihood.

Biometrics·2025
Same author

Learning Robust and Sparse Principal Components With the α-Divergence.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

Passive Radar Tracking in Clutter Using Range and Range-Rate Measurements.

Sensors (Basel, Switzerland)·2023
Same author

Coherent modeling of longitudinal causal effects on binary outcomes.

Biometrics·2022
Same journal

Simplifying debiased inference via automatic differentiation and probabilistic programming.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Principal stratification with U-statistics under principal ignorability.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Causal K-Means Clustering.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Correction to: Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
See all related articles

Related Experiment Video

Updated: May 8, 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

Marginal log-linear parameters for graphical Markov models.

Robin J Evans1, Thomas S Richardson

  • 1Department of Statistics, University of Washington.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|September 3, 2013
PubMed
Summary
This summary is machine-generated.

Marginal log-linear (MLL) models offer a flexible way to analyze multivariate discrete data. This study introduces a new class of MLL models linked to Acyclic Directed Mixed Graphs (ADMGs), detailing their variation independence properties.

Keywords:
acyclic directed mixed graphdiscrete graphical modelmarginal log-linear parameterparsimonious modellingvariation independence

Related Experiment Videos

Last Updated: May 8, 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

Area of Science:

  • Statistics
  • Computational Statistics
  • Discrete Data Analysis

Background:

  • Marginal log-linear (MLL) models are versatile for multivariate discrete data analysis.
  • MLL parametrizations under linear constraints can represent various statistical models, including those defined by conditional independences.
  • Acyclic Directed Mixed Graphs (ADMGs) offer a graphical representation for complex dependency structures.

Purpose of the Study:

  • To introduce and characterize a subclass of MLL models that correspond to ADMGs.
  • To determine the conditions under which the MLL parametrization for ADMGs is variation independent.
  • To provide a minimal constraint description for ADMG models using the MLL approach.

Main Methods:

  • Developing a subclass of Marginal Log-Linear (MLL) models.
  • Utilizing the global Markov property for Acyclic Directed Mixed Graphs (ADMGs).
  • Characterizing graph structures for variation independence in MLL parametrizations.

Main Results:

  • A novel subclass of MLL models corresponding to ADMGs is introduced.
  • Precise conditions for variation independence of the MLL parametrization for ADMGs are characterized.
  • The MLL approach provides the first minimal constraint description for ADMG models.

Conclusions:

  • The MLL approach offers a new framework for modeling with ADMGs.
  • The identified parametrization is adaptable to sparse modeling techniques.
  • The findings are illustrated with real-world data examples, demonstrating practical applicability.