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

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

1.1K
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...
1.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

213
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...
213
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

245
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...
245
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

211
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...
211

You might also read

Related Articles

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

Sort by
Same author

mRNA Expression of FGFR1 as Potential Marker for Predicting Prognosis of Surgical Resection of Small Cell Lung Cancer may be better than Protein Expression and Gene Amplification.

Journal of Cancer·2020
Same author

Effect of targeted nursing intervention on negative mental status and quality of life of elderly patients with coronary heart disease.

Minerva medica·2020
Same author

Self-co-attention neural network for anatomy segmentation in whole breast ultrasound.

Medical image analysis·2020
Same author

An improved two-step method for extraction and purification of primary cardiomyocytes from neonatal mice.

Journal of pharmacological and toxicological methods·2020
Same author

[Non-structural carbohydrate content of trees and its influencing factors at multiple spatial-temporal scales: A review].

Ying yong sheng tai xue bao = The journal of applied ecology·2020
Same author

Identification and characterization of Jingmen tick virus in rodents from Xinjiang, China.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2020

Related Experiment Video

Updated: Jan 2, 2026

Biological Preparation and Mechanical Technique for Determining Viscoelastic Properties of Zonular Fibers
06:39

Biological Preparation and Mechanical Technique for Determining Viscoelastic Properties of Zonular Fibers

Published on: December 16, 2021

2.3K

Nonparametric maximum likelihood estimation for the multisample Wicksell corpuscle problem.

Kwun Chuen Gary Chan1, Jing Qin2

  • 1Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A.

Biometrika
|June 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an expectation-maximization algorithm for estimating spherical radii distributions from mixed observational data. The method addresses measurement bias and sampling issues, improving estimation stability.

Keywords:
Abel-type integral equationExpectation-maximization algorithmIndirect measurementParticle size

More Related Videos

Assembly and Characterization of Polyelectrolyte Complex Micelles
08:44

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

11.4K
Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology
11:11

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Published on: June 10, 2014

12.0K

Related Experiment Videos

Last Updated: Jan 2, 2026

Biological Preparation and Mechanical Technique for Determining Viscoelastic Properties of Zonular Fibers
06:39

Biological Preparation and Mechanical Technique for Determining Viscoelastic Properties of Zonular Fibers

Published on: December 16, 2021

2.3K
Assembly and Characterization of Polyelectrolyte Complex Micelles
08:44

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

11.4K
Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology
11:11

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Published on: June 10, 2014

12.0K

Area of Science:

  • Statistics
  • Geometric Probability
  • Data Analysis

Background:

  • Estimating spherical radii distributions is challenging due to mixed observational data types.
  • Direct likelihood maximization is often intractable for such complex datasets.
  • Existing methods can suffer from numerical instability when dealing with indirect measurements and sampling bias.

Purpose of the Study:

  • To develop a robust nonparametric maximum likelihood estimator for spherical radii distributions.
  • To address challenges posed by mixtures of one-, two-, and three-dimensional observations.
  • To overcome limitations of existing methods, including numerical instability and indirect measurement problems.

Main Methods:

  • Utilizing an expectation-maximization (EM) algorithm for nonparametric maximum likelihood estimation.
  • Separately handling indirect measurement and sampling bias within the E- and M-steps.
  • Avoiding the need to solve numerically unstable Abel-type integral equations.

Main Results:

  • The proposed EM algorithm provides a stable and effective method for estimating spherical radii distributions.
  • The approach successfully manages mixed observational data, including biased and unbiased samples.
  • Extensions to ellipsoidal distributions and connections to multiplicative censoring were explored.

Conclusions:

  • The developed expectation-maximization algorithm offers a significant advancement in estimating spherical radii distributions from complex data.
  • This method provides a more numerically stable and versatile alternative to existing techniques.
  • The study opens avenues for further research in related areas like ellipsoidal data and censored observations.