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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.0K
Outliers and Influential Points01:08

Outliers and Influential Points

5.1K
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...
5.1K
What Are Outliers?01:12

What Are Outliers?

4.3K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.3K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
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...
7.1K
Modified Boxplots00:57

Modified Boxplots

8.0K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
8.0K
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

You might also read

Related Articles

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

Sort by
Same author

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same author

Scalable and robust regression models for continuous proportional data.

Journal of the American Statistical Association·2026
Same author

Local graph estimation with pathwise false discovery control.

Nature communications·2026
Same author

Hip, knee, and ankle running kinematics during stance for anterior cruciate ligament reconstruction patients.

Clinical biomechanics (Bristol, Avon)·2026
Same author

Bayesian Transfer Learning.

Statistical science : a review journal of the Institute of Mathematical Statistics·2026
Same author

Domain Adaptive Bootstrap Aggregating.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments.

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences·2025
Same journal

Spatiotemporal Interactive Modeling of Event-based Dynamic Networks.

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences·2025
Same journal

Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent.

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences·2025
Same journal

Anomaly Detection in Large-Scale Networks With Latent Space Models.

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences·2024
Same journal

Robust Low-rank Tensor Decomposition with the <math></math> Criterion.

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences·2024
Same journal

A Sharper Computational Tool for L<sub>2</sub>E Regression.

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences·2023
See all related articles

Related Experiment Video

Updated: May 4, 2026

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

2.3K

Bayesian Local Contamination Models for Multivariate Outliers.

Garritt L Page1, David B Dunson1

  • 1Department of Statistical Science, Duke University, Durham, NC 27704.

Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences
|December 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible local contamination model to establish reliable reference values from inter-laboratory studies, even with outlying lab data. The method effectively accommodates unusual multivariate observations, improving data analysis accuracy.

Keywords:
Bayesian robustnessComponent-wise classificationInter-laboratory studiesMixtures

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Related Experiment Videos

Last Updated: May 4, 2026

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

2.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Area of Science:

  • Statistics
  • Data Analysis
  • Metrology

Background:

  • Inter-laboratory studies often yield data with unusual observations from specific sources.
  • Establishing accurate reference values is crucial, but challenging when outlying data are present.

Purpose of the Study:

  • To propose a novel local contamination model for hierarchical data.
  • To accommodate unusual multivariate observations in inter-laboratory settings.
  • To develop a flexible method for establishing reference values with outlying labs.

Main Methods:

  • A hierarchical model incorporating a mixture at the process level.
  • The mixture includes a parametric component and a nonparametric contamination.
  • Random subsets of lab-specific mean vectors can be allocated to the contamination component.

Main Results:

  • The proposed local contamination model offers flexibility in handling multivariate outliers.
  • Simulation studies demonstrate the methodology's effectiveness compared to alternative approaches.
  • The method was successfully applied to a real-world inter-laboratory study.

Conclusions:

  • The developed local contamination model is a robust tool for inter-laboratory data analysis.
  • It provides a flexible and effective way to manage outlying observations.
  • This approach enhances the reliability of reference value determination in complex studies.