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...
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)...
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...
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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

Understanding drivers of early life course arts, culture and recreation participation in Aotearoa New Zealand.

Arts & health·2026
Same author

Sustainability for ship operations in seaport areas: Technical solutions and environmental assessment.

The Science of the total environment·2025
Same author

Splanchnic vein thrombosis in myeloproliferative neoplasms: risk factors for recurrences in a cohort of 181 patients.

Blood cancer journal·2016
Same author

Control of infectious mortality due to carbapenemase-producing Klebsiella pneumoniae in hematopoietic stem cell transplantation.

Bone marrow transplantation·2016
Same author

High rate of recurrent venous thromboembolism in patients with myeloproliferative neoplasms and effect of prophylaxis with vitamin K antagonists.

Leukemia·2016
Same author

Early molecular diagnosis of aspergillosis in a patient with acute myeloid leukaemia.

Heart, lung and vessels·2014
Same journal

Modeling Disease-specific Survival in Observational Studies with Missing Cause of Death: Leveraging Information from Clinical Trial Data.

Computational statistics & data analysis·2026
Same journal

A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models.

Computational statistics & data analysis·2025
Same journal

MIXANDMIX: numerical techniques for the computation of empirical spectral distributions of population mixtures.

Computational statistics & data analysis·2024
Same journal

Locally sparse quantile estimation for a partially functional interaction model.

Computational statistics & data analysis·2024
Same journal

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models.

Computational statistics & data analysis·2024
Same journal

GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction.

Computational statistics & data analysis·2024
See all related articles

Related Experiment Video

Updated: May 10, 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

Two algorithms for fitting constrained marginal models.

R J Evans1, A Forcina

  • 1Statistical Laboratory, University of Cambridge, UK.

Computational Statistics & Data Analysis
|June 25, 2013
PubMed
Summary
This summary is machine-generated.

This study compares two algorithms for discrete data models. The regression algorithm offers greater flexibility for individual-level covariates, while the Lagrangian method is more efficient for identically distributed data.

Keywords:
L1-penaltycategorical datamarginal log-linear modelmaximum likelihoodnon-linear constraint

More Related Videos

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Related Experiment Videos

Last Updated: May 10, 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

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Constrained marginal models are essential for analyzing discrete data.
  • Two primary algorithms, Lagrange multipliers and regression models, have been explored for fitting these models.

Purpose of the Study:

  • To conduct a detailed comparative analysis of the Lagrange multiplier and regression-based algorithms for fitting constrained marginal models to discrete data.
  • To investigate the efficiency and applicability of each method under different data conditions and modeling scenarios.

Main Methods:

  • Detailed theoretical analysis of the update steps for both Lagrange multiplier and regression-based algorithms.
  • Examination of algorithm performance with identically distributed observations.
  • Development and evaluation of a generalized regression algorithm to incorporate exogenous individual-level covariates.

Main Results:

  • The study demonstrates that the updates generated by both the Lagrange multiplier and regression methods are mathematically identical.
  • The Lagrangian method exhibits higher computational efficiency when dealing with identically distributed observations.
  • The generalized regression algorithm effectively models the impact of individual-level covariates, overcoming limitations of the Lagrangian approach in such contexts.

Conclusions:

  • Both algorithms are effective for fitting constrained marginal models, with distinct advantages depending on the data structure.
  • The regression-based approach provides a more scalable and feasible solution for models including individual-level covariates.
  • The research extends the applicability of these methods, including an extension to likelihood-based estimation with L1-penalties.