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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

280
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...
280
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

614
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
614
Multiple Regression01:25

Multiple Regression

4.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
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...
290
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.1K
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...
1.1K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

625
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
625

You might also read

Related Articles

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

Sort by
Same author

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.

PLoS genetics·2026
Same author

Strategies for collection, management, and release of data for multi-site longitudinal studies: Lessons from the ABCD Data Analysis, Informatics, & Resource Center.

Developmental cognitive neuroscience·2026
Same author

The longitudinal structure of cognition in the ABCD study and associations with neural function and psychopathology: A Bayesian probabilistic principal components analysis.

Developmental cognitive neuroscience·2026
Same author

Prenatal Substance Exposure and Birth Weight: Findings From the HEALthy Brain and Child Development Study.

Pediatrics·2026
Same author

A generalized synthetic control algorithm for sparse functional data.

bioRxiv : the preprint server for biology·2026
Same author

Developmental relations between internalizing symptoms and negative urgency during middle adolescence.

Development and psychopathology·2026
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: Feb 22, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

A Bayesian regression model for multivariate functional data.

Ori Rosen1, Wesley K Thompson2

  • 1Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA.

Computational Statistics & Data Analysis
|September 23, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian mixed-effects model for analyzing complex multivariate functional data. The model effectively handles unequally spaced observations and individual variations using advanced spline techniques and Markov chain Monte Carlo methods.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Related Experiment Videos

Last Updated: Feb 22, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Area of Science:

  • Statistics
  • Biostatistics
  • Functional Data Analysis

Background:

  • Analyzing multivariate functional data presents challenges due to unequally spaced observations and subject-specific variations.
  • Existing models may not adequately capture the complex dependencies and temporal dynamics inherent in such data.

Purpose of the Study:

  • To develop and present a flexible Bayesian mixed-effects model for the analysis of multivariate functional data with unequally spaced observation times.
  • To incorporate covariates into both fixed and random effects for a comprehensive analysis.
  • To utilize a multivariate Ornstein-Uhlenbeck process for the random error term.

Main Methods:

  • A Bayesian mixed-effects model framework was employed.
  • Low-rank cubic splines with radial basis functions were used for estimating mean functions and subject-specific deviations.
  • Markov chain Monte Carlo (MCMC) methods were utilized for model inference.

Main Results:

  • The proposed model effectively accommodates multivariate functional data with varying observation times across subjects.
  • The incorporation of covariates in both fixed and random effects allows for a nuanced understanding of influencing factors.
  • The use of the multivariate Ornstein-Uhlenbeck process provides a robust framework for modeling random error dynamics.

Conclusions:

  • The developed Bayesian model offers a powerful and flexible approach for analyzing complex multivariate functional data.
  • The method provides accurate estimation of mean functions and individual deviations, even with irregular data.
  • This approach enhances the understanding of underlying processes in fields utilizing functional data analysis.