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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.7K
Residual Plots01:07

Residual Plots

6.7K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
6.7K
Outliers and Influential Points01:08

Outliers and Influential Points

6.6K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.6K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.6K
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...
5.6K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

3.7K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Variable Selection and Regularization in Quantile Regression via Minimum Covariance Determinant Based Weights.

Entropy (Basel, Switzerland)·2021
Same author

Long memory mean and volatility models of platinum and palladium price return series under heavy tailed distributions.

SpringerPlus·2016
See all related articles

Related Experiment Video

Updated: Mar 16, 2026

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

11.0K

On studentized residuals in the quantile regression framework.

Edmore Ranganai1

  • 1Department of Statistics, University of South Africa, Roodepoort, South Africa.

Springerplus
|August 19, 2016
PubMed
Summary

This study introduces studentized regression quantiles (RQs) to simplify robust statistical methods. The externally studentized RQ diagnostic shows a dynamic pattern for detecting outliers, unlike the aggressive MAD-based method.

Keywords:
Elemental predictive residualElemental regressionElemental setLeverageOutlierRegression quantilesStudentized residual

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

1.3K
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

2.7K

Related Experiment Videos

Last Updated: Mar 16, 2026

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

11.0K
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

1.3K
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

2.7K

Area of Science:

  • Statistics
  • Robust Statistics
  • Econometrics

Background:

  • Regression quantiles (RQs) are gaining popularity but are less used than ordinary least squares due to perceived complexity.
  • Studentizing robust estimators is an ongoing effort to enhance their practical appeal.

Purpose of the Study:

  • To propose two versions of studentized regression quantiles (RQs) residual statistics: internally and externally studentized.
  • To compare the externally studentized RQs with the standardized median absolute deviation (MAD) method for outlier detection.

Main Methods:

  • Development of internally and externally studentized regression quantiles (RQs) residual statistics using the elemental set method.
  • Comparative analysis of the proposed RQ diagnostic against the MAD-based method on a known dataset.

Main Results:

  • The externally studentized RQ diagnostic exhibited a dynamic outlier detection pattern.
  • The standardized median absolute deviation (MAD) based diagnostic was found to be uniform and more aggressive in flagging outliers.

Conclusions:

  • The proposed externally studentized regression quantiles (RQs) offer a nuanced approach to outlier detection.
  • Studentized RQs provide a valuable alternative for practitioners seeking robust statistical methods.