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

4.3K
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...
4.3K
What Are Outliers?01:12

What Are Outliers?

5.4K
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...
5.4K
Outliers and Influential Points01:08

Outliers and Influential Points

6.5K
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...
6.5K
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

11.6K
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...
11.6K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Calcineurin-Dependent Stress Adaptation Enables Caspofungin Heteroresistance Leading to Stable Resistance in Candida Glabrata.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Neutrophil-derived S100A8/A9 impairs megakaryocyte maturation in immune thrombocytopenia.

Nature communications·2026
Same author

Expanded antigen-specific donor regulatory T cells for GVHD prevention.

Blood·2026
Same author

Metagenomic characterization and genetic profiling of hepatic viromes in Marmota himalayana from the Three-River-Source region of Qinghai Province.

BMC microbiology·2026
Same author

Product-Intrinsic NF-κB-Driven Transcriptional Programs Connote Durability of CAR-T Response in Multiple Myeloma.

Blood·2026
Same author

Risk Factors and Predictive Model of Poor Graft Function After Allogeneic Hematopoietic Stem Cell Transplantation.

Transplantation and cellular therapy·2026
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: Feb 28, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

692

Outlier Detection in Functional Data Using Adjusted Outlyingness.

Zhenghui Feng1, Xiaodan Hong2, Yingxing Li3

  • 1School of Science, Harbin Institute of Technology, Shenzhen 518055, China.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for detecting anomalies in functional data by projecting curves into a low-dimensional feature space. The approach effectively identifies subtle deviations, improving data integrity and anomaly discovery across various applications.

Keywords:
Mahalanobis distancedirectional outlyingnessfunctional datainformationnon-Gaussianoutlier detection

More Related Videos

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

18.8K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.6K

Related Experiment Videos

Last Updated: Feb 28, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

692
Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

18.8K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.6K

Area of Science:

  • Statistics
  • Signal Processing
  • Data Analysis

Background:

  • Anomaly detection is crucial for data integrity and reliable analysis.
  • Detecting outliers in functional data is challenging due to high dimensionality and subtle shape deformations.
  • Conventional methods often discretize curves, losing important variations.

Purpose of the Study:

  • To develop a novel framework for robust outlier detection in functional data.
  • To address the challenges posed by the infinite dimensionality of functional data.
  • To improve the accuracy and efficiency of anomaly identification in complex datasets.

Main Methods:

  • Projecting functional data into a low-dimensional feature space using a tailored weighting scheme.
  • Employing Mahalanobis distance for directional outlyingness detection under non-Gaussian assumptions.
  • Utilizing a robustified bootstrap resampling method with data-driven threshold determination.

Main Results:

  • The proposed framework demonstrated superior performance in simulations.
  • Achieved higher true positive rates and lower false positive rates for various outlier types.
  • Validated through practical applications in environmental, trajectory, and population data analysis.

Conclusions:

  • The novel framework offers a versatile and effective solution for functional data outlier detection.
  • The method enhances data cleaning and facilitates the discovery of anomalous events.
  • Its cross-domain applicability highlights its practical utility in diverse scientific fields.