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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
Multiple Regression01:25

Multiple Regression

3.0K
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...
3.0K
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

513
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...
513
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Oxymatrine alleviates cerebral ischemia-reperfusion injury by inhibiting microglia ferroptosis via NRF2 pathway activation.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

4d orbital ruthenium doping enables high-capacity and stable α-MnO<sub>2</sub> cathodes for aqueous zinc-ion batteries.

Dalton transactions (Cambridge, England : 2003)·2026
Same author

Activation of mitophagy via miR-223/NLRP3 axis ameliorates dopaminergic neuronal damage in parkinson's disease.

Metabolic brain disease·2026
Same author

Long-term Retrospective Study on Survival and Cardiac Function Improvement in Heart Failure Patients with Atrial Fibrillation Treated With Radiofrequency Ablation.

Kardiologiia·2026
Same author

Groundwater Chemistry and Children's Blood Lead Levels: A County-Wise Analysis in the United States.

GeoHealth·2026
Same author

Ambient Temperature and Risk of Renal Colic: A Systematic Review and Meta-analysis.

Kidney medicine·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: Jul 6, 2025

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

10.7K

Additive partially linear model for pooled biomonitoring data.

Xichen Mou1, Dewei Wang2

  • 1Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN 38152, U.S.A.

Computational Statistics & Data Analysis
|January 8, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new regression method for human biomonitoring, making toxic chemical analysis more cost-effective. The approach uses pooled samples to assess chemical exposure and health risks efficiently.

Keywords:
Additive partially linear modelBiomarkersHomogeneous poolingLocal linear fitNHANESPooled biospecimens

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays
06:13

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Published on: February 21, 2020

6.6K

Related Experiment Videos

Last Updated: Jul 6, 2025

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

10.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays
06:13

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Published on: February 21, 2020

6.6K

Area of Science:

  • Environmental Health
  • Biomonitoring
  • Statistical Modeling

Background:

  • Human biomonitoring assesses health by measuring chemical accumulation in biological samples.
  • High analytical costs necessitate cost-effective strategies like specimen pooling.
  • Interpreting pooled data requires advanced statistical techniques.

Purpose of the Study:

  • To develop a novel regression framework for analyzing pooled human biomonitoring data.
  • To extend the additive partially linear model (APLM) for pooled sample analysis.
  • To enable accurate interpretation of toxic substance concentrations in aggregated specimens.

Main Methods:

  • Proposed a new regression framework by extending the additive partially linear model (APLM).
  • Developed consistent estimators for the APLM using an iterative disaggregation process.
  • Evaluated the method's performance through simulations and real-world data analysis.

Main Results:

  • The extended APLM effectively handles the complexities of pooled biomonitoring data.
  • Consistent estimators were successfully obtained for the proposed model.
  • The framework demonstrated reliable performance in simulations and a practical application.

Conclusions:

  • The novel APLM-based regression framework offers a cost-effective solution for human biomonitoring.
  • This method enhances the ability to assess chemical exposure and associated health risks from pooled samples.
  • The approach is valuable for environmental health studies utilizing aggregated biomonitoring data.