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

356
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...
356
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
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

288
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...
288
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

111
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
111
Linearization and Approximation01:26

Linearization and Approximation

85
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
85
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

273
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...
273

You might also read

Related Articles

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

Sort by
Same author

HPV-Adjusted Feature Screening With FDR Control in Head and Neck Cancer.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.

Journal of nonparametric statistics·2026
Same author

MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models.

Journal of agricultural, biological, and environmental statistics·2026
Same author

Biosensing in Healthcare Applications.

Studies in health technology and informatics·2026
Same author

Prognostic value of FDG-PET SUV changes in cervical cancer following radiation therapy: a retrospective cohort study.

Archives of gynecology and obstetrics·2026
Same author

Cardiovascular Risk Factors Among Younger and Older C-AYA Cancer Survivors Treated with Anthracyclines: A Single-Center Analysis.

Cancers·2026
Same journal

Improving Overall Risk Ranking via Subgroup-Level Information Borrowing in Survival Risk Stratification.

Statistics and its interface·2026
Same journal

High-dimensional Bayesian mediation analysis with adaptive Laplace priors.

Statistics and its interface·2026
Same journal

Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.

Statistics and its interface·2025
Same journal

Variable selection for doubly robust causal inference.

Statistics and its interface·2025
Same journal

Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data.

Statistics and its interface·2025
Same journal

A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.

Statistics and its interface·2025
See all related articles

Related Experiment Video

Updated: Feb 19, 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

3.8K

Quantile regression in linear mixed models: a stochastic approximation EM approach.

Christian E Galarza1, Victor H Lachos2, Dipankar Bandyopadhyay3

  • 1Departamento de Matemáticas, Escuela Superior Politécnica del Litoral, ESPOL, Ecuador.

Statistics and Its Interface
|November 7, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing longitudinal data using quantile regression (QR) and the asymmetric Laplace distribution (ALD). The proposed Stochastic Approximation of the EM (SAEM) algorithm provides more accurate estimates than existing methods.

Keywords:
Asymmetric laplace distributionLinear mixed-effects modelsQuantile regressionSAEM algorithm

More Related Videos

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

6.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Feb 19, 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

3.8K
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

6.0K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Quantile regression (QR) offers a more comprehensive analysis of outcome variables than traditional mean regression.
  • QR is robust to outliers and distributional misspecification.
  • Analyzing continuous longitudinal data requires specialized statistical methods.

Purpose of the Study:

  • To develop a likelihood-based approach for quantile regression (QR) in continuous longitudinal data analysis.
  • To implement a Stochastic Approximation of the EM (SAEM) algorithm for estimating parameters in QR models.
  • To compare the performance of the SAEM algorithm against existing methods.

Main Methods:

  • Utilized the asymmetric Laplace distribution (ALD) for quantile regression modeling.
  • Developed a Stochastic Approximation of the EM (SAEM) algorithm for maximum likelihood estimation.
  • Evaluated finite sample performance and asymptotic properties through simulations and real-world data.

Main Results:

  • The SAEM algorithm accurately estimates fixed-effects and variance components.
  • SAEM estimates demonstrated superior performance (lower standard errors and mean square error) compared to the Geraci and Bottai (2014) approach.
  • The algorithm is implemented in the R package qrLMM().

Conclusions:

  • The proposed SAEM algorithm is an efficient and robust method for analyzing continuous longitudinal data using quantile regression.
  • This approach enhances the analysis of the entire conditional distribution, offering advantages over mean regression.
  • The R package qrLMM() facilitates the application of this advanced statistical technique.