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

118
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...
118
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K
Multiple Regression01:25

Multiple Regression

3.3K
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...
3.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

701
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...
701
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

792
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...
792
Regression Analysis01:11

Regression Analysis

6.4K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.4K

You might also read

Related Articles

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

Sort by
Same author

Time trends in post-polypectomy surveillance guideline adherence: analysis of over 90 000 colonoscopies with polypectomy.

Endoscopy·2025
Same author

Association between CMR-derived hepatic T1-time, tricuspid regurgitation and survival.

European journal of clinical investigation·2025
Same author

Penalized regression splines in Mixture Density Networks.

The international journal of biostatistics·2025
Same author

Effect of Cecal Intubation Rate on Post Colonoscopy Colorectal Cancer Deaths and Detection of Colorectal Cancer Precursors.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2025
Same author

Probabilistic Topic Modeling With Transformer Representations.

IEEE transactions on neural networks and learning systems·2025
Same author

Comparative Assessment of CMR-Determined Extracellular Volume Metrics in Predicting Adverse Outcomes.

Journal of clinical medicine·2025
Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026
Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026
Same journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026
Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026
See all related articles

Related Experiment Video

Updated: Oct 12, 2025

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

3.5K

Gradient boosting for linear mixed models.

Colin Griesbach1, Benjamin Säfken2, Elisabeth Waldmann1

  • 1Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

The International Journal of Biostatistics
|November 26, 2021
PubMed
Summary
This summary is machine-generated.

We introduce a novel gradient boosting algorithm that improves upon existing methods for mixed models. This new approach provides unbiased estimation of random effects and their variance structure for longitudinal and clustered data.

Keywords:
gradient boostingmixed modelsregularised regressionstatistical learning

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.4K

Related Experiment Videos

Last Updated: Oct 12, 2025

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

3.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.4K

Area of Science:

  • Statistical Learning
  • Machine Learning
  • Biostatistics

Background:

  • Gradient boosting is a powerful statistical learning framework for regression and classification.
  • Current boosting methods for mixed models have limitations, including unbalanced effect selection and biased random effect estimates.
  • These limitations hinder accurate analysis of longitudinal and clustered data.

Purpose of the Study:

  • To propose a new gradient boosting algorithm that addresses the flaws in existing methods for mixed models.
  • To provide unbiased estimation of random effects and their variance structure.
  • To improve the fitting process for longitudinal and clustered data.

Main Methods:

  • Developed a novel boosting algorithm that explicitly accounts for random structure by excluding it from the selection procedure.
  • Implemented a method for correcting random effects estimates.
  • Incorporated likelihood-based estimation for the random effects variance structure.

Main Results:

  • The proposed algorithm demonstrates an unbiased fitting approach.
  • Simulations and data examples confirm the effectiveness of the new method.
  • Achieved proper correction of random effects estimates and accurate variance structure estimation.

Conclusions:

  • The new boosting algorithm offers a superior approach for analyzing mixed models with random effects.
  • It overcomes limitations of current methods, leading to more reliable predictions for longitudinal and clustered data.
  • This advancement enhances the application of gradient boosting in statistical modeling.