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

Random and Systematic Errors01:20

Random and Systematic Errors

11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K
Random and Systematic Errors01:20

Random and Systematic Errors

972
972
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

8.7K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
8.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

93.8K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
93.8K
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

You might also read

Related Articles

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

Sort by
Same author

Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.

Frontiers in digital health·2026
Same author

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same author

Reply to Z Yu and F Qin.

The American journal of clinical nutrition·2026
Same author

Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.

Statistics in medicine·2026
Same author

Generative AI-assisted Bayesian-frequentist Hybrid Inference in Single-cell RNA Sequencing Analysis for Genes Associated with Alzheimer's Disease.

medRxiv : the preprint server for health sciences·2026
Same author

Association between high likelihood of obstructive sleep apnea and masked hypertension: findings from the Jackson heart and coronary artery risk development in young adults studies.

Journal of hypertension·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

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

6.1K

Multiple indicators, multiple causes measurement error models.

Carmen D Tekwe1, Randy L Carter, Harry M Cullings

  • 1Department of Epidemiology and Biostatistics, Texas A&M University, 1266 TAMU, College Station, TX, 77843-1266, U.S.A.

Statistics in Medicine
|June 26, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces the MIMIC measurement error (MIMIC ME) model to account for errors in observed causes of latent variables. The model was applied to atomic bomb survivor data, analyzing radiation dose and dyslipidemia impacts.

Keywords:
Berkson errorMIMIC modelsatomic bomb survivor datadyslipidemiainstrumental variableslatent variablesmeasurement error

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
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

2.9K

Related Experiment Videos

Last Updated: Apr 27, 2026

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

6.1K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
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

2.9K

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Multiple Indicators Multiple Causes (MIMIC) models are used to study latent variables influencing outcomes.
  • Classical MIMIC models assume observed causes of latent variables are error-free, which is often not the case.
  • Measurement error in causal variables can bias results in latent variable modeling.

Purpose of the Study:

  • To extend the classical linear MIMIC model to incorporate both Berkson and classical measurement errors, creating the MIMIC measurement error (MIMIC ME) model.
  • To develop likelihood-based estimation methods for the novel MIMIC ME model.
  • To apply the MIMIC ME model to analyze the effects of dyslipidemia and radiation dose on physical manifestations in atomic bomb survivor data.

Main Methods:

  • Development of the MIMIC measurement error (MIMIC ME) model, extending the classical linear MIMIC model.
  • Implementation of likelihood-based estimation techniques for the MIMIC ME model.
  • Application of the MIMIC ME model to a dataset of atomic bomb survivors.

Main Results:

  • The study successfully developed and estimated the MIMIC ME model, accommodating measurement errors in causal variables.
  • Application to atomic bomb survivor data provided insights into the impact of dyslipidemia and radiation dose.
  • A byproduct of the analysis yielded a data-driven estimate for the variance of classical measurement error in radiation dose estimates.

Conclusions:

  • The MIMIC ME model offers a robust framework for analyzing latent variables when causal indicators are subject to measurement error.
  • The application demonstrates the model's utility in epidemiological research, particularly with historical exposure data.
  • The findings contribute to a better understanding of radiation exposure effects and measurement error in dose estimation.