Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Prediction Intervals

Prediction Intervals

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.

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

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...

You might also read

Related Articles

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

Sort by

Same author

Microbially Induced Corrosion: A Hidden Risk in Industrial Accidents.

ACS omega·2026

Same author

Conceptualisation of Digital Wellbeing Associated with Generative Artificial Intelligence from the Perspective of University Students.

European journal of investigation in health, psychology and education·2025

Same author

Characterization of Hemoglobin Variants by Capillary Electrophoresis, UV-Vis, and FTIR Spectroscopy.

Electrophoresis·2025

Same author

Sparse solution of least-squares twin multi-class support vector machine using ℓ<sub>0</sub> and ℓ<sub>p</sub>-norm for classification and feature selection.

Neural networks : the official journal of the International Neural Network Society·2023

Same author

Educational Psychology Aspects of Learning with Chatbots without Artificial Intelligence: Suggestions for Designers.

European journal of investigation in health, psychology and education·2023

Same author

Inverse free reduced universum twin support vector machine for imbalanced data classification.

Neural networks : the official journal of the International Neural Network Society·2022

Search research articles

Related Experiment Video

Updated: May 1, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Tolerance approach to possibilistic nonlinear regression with interval data.

Milan Hladík, Michal Cerný

IEEE Transactions on Cybernetics

|April 2, 2014

Summary

This study introduces a novel method for possibilistic nonlinear regression, enabling precise interval regression parameters for both crisp and interval data. The approach ensures accurate data coverage by the nonlinear interval regression function.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Related Experiment Videos

Last Updated: May 1, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Area of Science:

Statistics
Data Science
Computational Intelligence

Background:

Possibilistic regression models handle uncertainty in data.
Nonlinear models are crucial for complex data relationships.
Interval data requires specialized regression techniques.

Purpose of the Study:

To develop methods for computing tight interval regression parameters for possibilistic nonlinear models.
To ensure all observed crisp or interval data are covered by the nonlinear interval regression function.
To extend existing tolerance approaches to the nonlinear interval regression case.

Main Methods:

Proposing a tolerance-based approach for determining interval regression parameters.
Defining specific classes of nonlinear regression models with efficient algorithms.
Developing extensions for calculating bounds on parameter widths for other models.

Main Results:

An efficient method for determining interval regression parameters in possibilistic nonlinear models.
Algorithms for specific classes of nonlinear models.
Extensions providing bounds on parameter widths for broader applicability.

Conclusions:

The proposed tolerance-based method effectively computes tight interval regression parameters for possibilistic nonlinear models.
The study provides efficient computational tools for handling interval and crisp data in nonlinear regression.
The research contributes to the advancement of uncertainty quantification in regression analysis.