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

Outliers and Influential Points01:08

Outliers and Influential Points

4.2K
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...
4.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.9K
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...
1.9K
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
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...
7.8K
Regression Analysis01:11

Regression Analysis

6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
Cluster Sampling Method01:20

Cluster Sampling Method

12.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.6K

You might also read

Related Articles

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

Sort by
Same author

Temporal Trends in the Epidemiology of HIV in Turkey.

Current HIV research·2020
Same author

Integrase Strand Transfer Inhibitors (INSTIs) Resistance Mutations in HIV-1 Infected Turkish Patients.

HIV clinical trials·2016
Same author

The course of spinal tuberculosis (Pott disease): results of the multinational, multicentre Backbone-2 study.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2015
Same author

Comparison of selective and enrichment media for isolation of vancomycin-resistant enterococci from rectal swab specimens.

Indian journal of medical microbiology·2015
Same author

Antitumor effects of Origanum acutidens extracts on human breast cancer.

Journal of B.U.ON. : official journal of the Balkan Union of Oncology·2013
Same author

Bone marrow infection caused by Mycobacterium avium complex in a patient with systemic lupus erythematosus.

Lupus·2009

Related Experiment Video

Updated: Sep 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Binary particle swarm optimization as a detection tool for influential subsets in linear regression.

G Deliorman1, D Inan2

  • 1Faculty of Engineering and Architecture, Software Engineering, Beykoz University, Istanbul, Turkey.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

This study introduces a novel method for detecting influential observations in data analysis. The new approach, using binary particle swarm optimization, overcomes limitations of existing methods, offering improved statistical validity.

Keywords:
Influential subsetsbinary particle swarm optimizationdiagnosticsheuristic algorithmslinear regression

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Sep 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Statistics
  • Data Analysis
  • Computational Statistics

Background:

  • Influential observations significantly impact regression analysis, reducing statistical validity.
  • Existing methods for identifying influential observations suffer from masking and swamping effects and require distributional assumptions.
  • Detecting influential subsets remains a challenge for current diagnostic tools.

Purpose of the Study:

  • To develop a new, robust diagnostic tool for identifying influential observations.
  • To overcome the limitations of existing methods, including masking, swamping, and distributional assumptions.
  • To enhance the reliability of statistical analyses in the presence of outliers.

Main Methods:

  • Utilizing the meta-heuristic binary particle swarm optimization (BPSO) algorithm.
  • Developing a novel diagnostic approach for influential observation detection.
  • Applying the BPSO algorithm without requiring distributional assumptions.

Main Results:

  • The proposed BPSO-based method effectively identifies influential observations.
  • The new approach is not susceptible to masking and swamping effects.
  • Simulations and real-world data applications demonstrate the method's performance.

Conclusions:

  • The developed BPSO algorithm offers a superior alternative for identifying influential observations.
  • This method enhances statistical analysis robustness by mitigating outlier impact.
  • The tool provides a reliable solution for detecting influential data points without prior distributional knowledge.