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

1.2K
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...
1.2K
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
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
Stratified Sampling Method01:16

Stratified Sampling Method

15.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
15.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.7K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.7K
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

Analyzing Complex Educational Data: A Data Analytic Framework for Integrating Structured and Unstructured Eye-Tracking Data.

Psychometrika·2026
Same author

Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models.

Psychometrika·2026
Same author

A general dynamic learning model framework for cognitive diagnosis.

The British journal of mathematical and statistical psychology·2025
Same author

A two-step item bank calibration strategy based on 1-bit matrix completion for small-scale computerized adaptive testing.

The British journal of mathematical and statistical psychology·2024
Same author

Flexible Computerized Adaptive Tests to Detect Misconceptions and Estimate Ability Simultaneously.

Applied psychological measurement·2020
Same author

Using Response Times and Response Accuracy to Measure Fluency Within Cognitive Diagnosis Models.

Psychometrika·2020

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

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model.

Shiyu Wang1

  • 1University of Georgia, Athens, Georgia, USA.

The British Journal of Mathematical and Statistical Psychology
|October 29, 2017
PubMed
Summary

This study examines how classification accuracy in cognitive diagnosis models (CDMs) is affected by model misspecification. It finds that consistent classification is possible even with misspecified models under specific conditions.

Keywords:
Q matrixcognitive diagnosislarge sample theorylatent class modelmaximum likelihood estimationmodel misspecification

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

977

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
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

977

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Maximum likelihood classification is standard for cognitive diagnosis models (CDMs).
  • Asymptotic behavior of CDMs is understood under correct specification.
  • Behavior under model misspecification, particularly in latent class models, is less explored.

Purpose of the Study:

  • Investigate the asymptotic behavior of a two-stage maximum likelihood classifier under a misspecified CDM.
  • Determine conditions for consistent classification with misspecified models.
  • Provide guidelines for using misspecified models in classification.

Main Methods:

  • Analysis within a general restricted latent class model framework.
  • Theoretical investigation of asymptotic properties.
  • Simulation studies and real data application.

Main Results:

  • Sufficient conditions for consistent classification using misspecified CDMs are proposed.
  • Scenarios leading to inconsistent classification are identified.
  • Empirical evidence supports theoretical findings.

Conclusions:

  • Misspecified CDMs can yield consistent classification under certain conditions.
  • Findings offer practical guidance on model selection for classification tasks.
  • Understanding model misspecification is crucial for reliable cognitive diagnosis.