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

Bone Markings01:26

Bone Markings

7.9K
Bones have various surface features that help form joints and attach to other soft tissues. Depending on the function, bone markings are categorized into articulating projections, processes for attachment, depressions, and openings.
Articulating Projections
Articulating projections are found where two bones meet to form a joint. These structures are usually found at the ends of bones. The largest articulation is a rounded projection called the head, supported by a narrow neck at the ends of...
7.9K
Design Example: Marking Boundaries of a Site Using a Compass01:12

Design Example: Marking Boundaries of a Site Using a Compass

286
Marking site boundaries using a compass is a precise surveying technique that ensures the accuracy of boundary delineation. The process begins by using provided site details, including the bearings and lengths of each boundary line. The initial step involves calculating latitudes and departures for all sides of the site. This computation verifies that the traverse is free of errors, ensuring a closed and accurate boundary.The process starts at a known point, such as Point A, which is often...
286
Correlations02:20

Correlations

35.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.8K
Correlation and Causation01:27

Correlation and Causation

42.1K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.1K
Correlation01:09

Correlation

14.8K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
14.8K
Correlation and Regression00:53

Correlation and Regression

3.1K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Do Monetary Incentives Influence Users' Behavior in Participatory Sensing?

Sensors (Basel, Switzerland)·2018
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 2026

The Bionic Clicker Mark I & II
08:23

The Bionic Clicker Mark I & II

Published on: August 14, 2017

16.7K

Inhomogeneous mark correlation functions for general marked point processes.

Mehdi Moradi1, Matthias Eckardt2

  • 1Department of Mathematics and Mathematical Statistics, Umeå University, 90187 Umeå, Sweden.

Biometrics
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces inhomogeneous mark correlation functions to analyze spatial data, revealing patterns in environmental and biological contexts. These new methods accurately capture mark associations and variations, outperforming traditional approaches in complex spatial distributions.

Keywords:
Inhomogeneous mark correlationLongleafPfynwaldinhomogeneous mark Variograminhomogeneous pair correlation functionintensity function

More Related Videos

Administering and Detecting Protein Marks on Arthropods for Dispersal Research
10:30

Administering and Detecting Protein Marks on Arthropods for Dispersal Research

Published on: January 28, 2016

7.8K
Generation of Marked and Markerless Mutants in Model Cyanobacterial Species
11:45

Generation of Marked and Markerless Mutants in Model Cyanobacterial Species

Published on: May 29, 2016

12.6K

Related Experiment Videos

Last Updated: Jan 22, 2026

The Bionic Clicker Mark I & II
08:23

The Bionic Clicker Mark I & II

Published on: August 14, 2017

16.7K
Administering and Detecting Protein Marks on Arthropods for Dispersal Research
10:30

Administering and Detecting Protein Marks on Arthropods for Dispersal Research

Published on: January 28, 2016

7.8K
Generation of Marked and Markerless Mutants in Model Cyanobacterial Species
11:45

Generation of Marked and Markerless Mutants in Model Cyanobacterial Species

Published on: May 29, 2016

12.6K

Area of Science:

  • Ecology
  • Spatial Statistics
  • Environmental Science

Background:

  • Spatial phenomena often exhibit uneven distributions and space-dependent variations.
  • Traditional methods struggle to accurately analyze mark associations in spatially inhomogeneous contexts.

Purpose of the Study:

  • Introduce inhomogeneous mark correlation functions to quantify mark associations/variations in space.
  • Develop and evaluate nonparametric estimators for these functions.
  • Compare the performance of new methods against traditional approaches.

Main Methods:

  • Developed nonparametric estimators for inhomogeneous mark correlation functions.
  • Conducted simulation studies across various spatial scenarios (nonstationary, clustering, sparse).
  • Applied the functions to analyze Longleaf and Scots pine tree data.

Main Results:

  • Inhomogeneous mark correlation functions accurately identify mark associations and their spatial range.
  • New methods outperform traditional approaches in spatially inhomogeneous settings.
  • Intensity estimation methods have minimal impact on estimator bias/variance.

Conclusions:

  • Inhomogeneous mark correlation functions provide superior insights into spatial patterns compared to traditional methods.
  • The approach is effective for analyzing marked point patterns in ecological studies.
  • Demonstrated utility in analyzing tree growth patterns in distinct forest ecosystems.