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

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

You might also read

Related Articles

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

Sort by
Same author

Independent prognostic value of peak width of skeletonized mean diffusivity on clinical progression in Alzheimer's disease.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.

Scientific data·2026
Same author

Improving metagenome binning by integrating intrinsic features and taxonomy.

Nature biotechnology·2026
Same author

Artificial intelligence for detecting acute heart failure on chest CT: prospective clinical proof-of-concept validation.

European radiology experimental·2026
Same author

Risks of automation in medicine - a review article for the obstetrics case.

Danish medical journal·2026
Same author

Cross-disorder comparison of brain structures among 4836 individuals with mental disorders and controls utilizing danish population-based clinical MRI scans.

Molecular psychiatry·2026

Related Experiment Video

Updated: May 15, 2026

Tree Core Analysis with X-ray Computed Tomography
06:56

Tree Core Analysis with X-ray Computed Tomography

Published on: September 22, 2023

Toward a theory of statistical tree-shape analysis.

Aasa Feragen1, Pechin Lo, Marleen de Bruijne

  • 1eScience Center, Department of Computer Science, University of Copenhagen, Universitetsparken 5, 2011 Copenhagan, Denmark. aasa@diku.dk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 26, 2012
PubMed
Summary

We developed a new framework for statistical analysis of tree-shaped data. The Quotient Euclidean Distance (QED) metric offers superior geometric properties for analyzing tree shapes compared to Tree Edit Distance (TED).

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: May 15, 2026

Tree Core Analysis with X-ray Computed Tomography
06:56

Tree Core Analysis with X-ray Computed Tomography

Published on: September 22, 2023

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Computational geometry
  • Statistical shape analysis
  • Graph theory

Background:

  • Statistical analysis of tree-shaped data is challenging due to complex shape spaces.
  • Existing metrics like Tree Edit Distance (TED) lack desirable geometric properties for statistical inference.
  • A need exists for robust metrics and frameworks for analyzing tree structures.

Purpose of the Study:

  • To develop a statistical framework for analyzing tree shapes.
  • To introduce and evaluate a new metric, Quotient Euclidean Distance (QED), on this shape space.
  • To compare QED with the classical Tree Edit Distance (TED) for statistical applicability.

Main Methods:

  • Construction of a shape space framework for tree-shapes.
  • Application of Gromov's metric geometry to analyze the properties of TED and QED.
  • Theoretical analysis of geodesic existence and uniqueness for QED.
  • Experimental validation using synthetic and real-world data (pulmonary CT scans).

Main Results:

  • The proposed shape space framework reveals singularities corresponding to topological transitions.
  • The new metric QED exhibits favorable geometric properties: existing and locally unique geodesics.
  • QED allows for the existence and generic local uniqueness of average trees, crucial for statistical analysis.
  • TED lacks these advantageous geometric properties despite algorithmic benefits.

Conclusions:

  • The developed framework and the QED metric provide a theoretically sound basis for statistical tree-shape analysis.
  • QED demonstrates superior geometric properties essential for robust statistical inference on tree structures.
  • The framework shows promise for applications in diverse fields requiring analysis of branching structures.