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

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

580
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
580
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

754
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
754
Regression Analysis01:11

Regression Analysis

5.9K
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:
5.9K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.2K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Agent-based models under uncertainty.

F1000Research·2024
Same author

Comparative, collaborative, and integrative risk governance for emerging technologies.

Environment systems & decisions·2023
Same author

Belongingness challenged: Exploring the impact on older adults during the COVID-19 pandemic.

PloS one·2022
Same author

Is Protecting Older Adults from COVID-19 Ageism? A Comparative Cross-cultural Constructive Grounded Theory from the United Kingdom and Colombia.

The Journal of social issues·2022
Same author

Correction: Is no test better than a bad test: Impact of diagnostic uncertainty on the spread of COVID-19.

PloS one·2021
Same author

Is "no test is better than a bad test"? Impact of diagnostic uncertainty in mass testing on the spread of COVID-19.

PloS one·2020
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Aug 9, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Neural network model for imprecise regression with interval dependent variables.

Krasymyr Tretiak1, Georg Schollmeyer2, Scott Ferson1

  • 1University of Liverpool, Liverpool L69 7ZX, United Kingdom.

Neural Networks : the Official Journal of the International Neural Network Society
|February 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel iterative method for regression analysis, handling interval data to quantify epistemic uncertainty. The approach employs interval neural networks for robust interval predictions, improving uncertainty quantification in regression models.

Keywords:
Imprecise regressionInterval dataNeural networkUncertainty

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

Related Experiment Videos

Last Updated: Aug 9, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

Area of Science:

  • Computational statistics
  • Machine learning
  • Data analysis

Background:

  • Regression analysis often assumes precise data points.
  • Epistemic uncertainty in output variables requires specialized handling.
  • Existing methods may not adequately address interval-valued dependent variables.

Purpose of the Study:

  • To develop a computationally feasible method for rigorous bounds on interval-generalization in regression.
  • To account for epistemic uncertainty in output variables using interval data.
  • To introduce an iterative approach using machine learning for imprecise regression models.

Main Methods:

  • Utilized a single-layer interval neural network trained on interval data.
  • Employed first-order gradient-based optimization and interval analysis computations.
  • Developed an iterative method to estimate bounds of the expectation region for regression lines.

Main Results:

  • Successfully fitted an imprecise regression model to interval data.
  • Achieved interval predictions by minimizing mean squared error between actual and predicted intervals.
  • Demonstrated the method's capability to model measurement imprecision without probabilistic information.

Conclusions:

  • The proposed iterative method provides rigorous bounds for interval-generalization in regression analysis.
  • The interval neural network approach effectively handles epistemic uncertainty in output variables.
  • The method extends to multi-layer neural networks, offering a versatile tool for imprecise data analysis.