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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Binomial Probability Distribution01:15

Binomial Probability Distribution

11.0K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
11.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

450
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...
450
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.9K
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...
7.9K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K
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

531
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...
531

You might also read

Related Articles

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

Sort by
Same author

A new robust approach for the polytomous logistic regression model based on Rényi's pseudodistances.

Biometrics·2024
Same author

Robust inference for skewed data in health sciences.

Journal of applied statistics·2022
Same author

A robust variable screening procedure for ultra-high dimensional data.

Statistical methods in medical research·2021
Same author

Robust Wald-type tests under random censoring.

Statistics in medicine·2020
Same author

Model Selection in a Composite Likelihood Framework Based on Density Power Divergence.

Entropy (Basel, Switzerland)·2020
Same author

A Generalized Relative (<i>α</i>, <i>β</i>)-Entropy: Geometric Properties and Applications to Robust Statistical Inference.

Entropy (Basel, Switzerland)·2020
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

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

3.4K

Robust Minimum Divergence Estimation for the Multinomial Circular Logistic Regression Model.

Elena Castilla1, Abhik Ghosh2

  • 1Departamento de Matematica Aplicada, Rey Juan Carlos University, Mostoles Campus, 28933 Madrid, Spain.

Entropy (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces robust estimators for circular logistic regression models, enhancing data analysis reliability in natural and social sciences. The new methods improve accuracy even with contaminated data, crucial for fields like forestry and meteorology.

Keywords:
circular regressiondensity power divergencerobust estimation

More Related Videos

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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Related Experiment Videos

Last Updated: Jul 12, 2025

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

3.4K
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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Area of Science:

  • Statistics
  • Circular Data Analysis
  • Regression Modeling

Background:

  • Circular data are vital across natural and social sciences, including forestry and meteorology.
  • Traditional maximum likelihood methods are sensitive to data contamination, limiting their reliability.
  • Robust statistical inference is needed for analyzing circular data, especially with potential outliers.

Purpose of the Study:

  • To develop robust estimators for multinomial circular logistic regression models with multiple circular covariates.
  • To extend the density-power-divergence estimation approach to this specific model class.
  • To investigate the asymptotic properties and practical performance of the proposed robust estimators.

Main Methods:

  • Development of robust estimators based on the density-power-divergence framework.
  • Theoretical analysis of the asymptotic properties of the new estimators.
  • Extensive simulation studies to evaluate robustness and performance.

Main Results:

  • The proposed density-power-divergence-based estimators demonstrate robustness against data contamination.
  • Asymptotic properties of the robust estimators are theoretically established.
  • The estimators perform well in simulations and real-world data examples.

Conclusions:

  • The developed robust estimators offer a reliable alternative for analyzing circular logistic regression models.
  • These methods are particularly valuable when data contamination is suspected.
  • The approach is applicable to diverse fields utilizing circular data, such as forest science and meteorology.