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

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

25.2K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
25.2K
Entropy02:39

Entropy

30.6K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
30.6K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

3.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
3.0K
Ranks01:02

Ranks

273
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
273
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.5K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
5.5K
Third Law of Thermodynamics02:38

Third Law of Thermodynamics

19.3K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
19.3K

You might also read

Related Articles

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

Sort by
Same author

Characterizing Complex Spatiotemporal Patterns from Entropy Measures.

Entropy (Basel, Switzerland)·2024
Same author

The asymptotic distribution of the permutation entropy.

Chaos (Woodbury, N.Y.)·2023
Same author

Asymptotic Distribution of Certain Types of Entropy under the Multinomial Law.

Entropy (Basel, Switzerland)·2023
Same author

Entropy Estimators in SAR Image Classification.

Entropy (Basel, Switzerland)·2022
Same author

Estrogen Replacement Therapy Induces Antioxidant and Longevity-Related Genes in Women after Medically Induced Menopause.

Oxidative medicine and cellular longevity·2021
Same author

Author Correction: A detailed characterization of complex networks using Information Theory.

Scientific reports·2021

Related Experiment Video

Updated: Aug 19, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.8K

Statistical properties of the entropy from ordinal patterns.

E T C Chagas1, A C Frery2, J Gambini3

  • 1Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG 30123-970, Brazil.

Chaos (Woodbury, N.Y.)
|December 1, 2022
PubMed
Summary

This study characterizes the asymptotic distribution of empirical Shannon entropy for time series. A new bilateral test for comparing Shannon entropy between signals was developed and applied to temperature data.

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.4K

Related Experiment Videos

Last Updated: Aug 19, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.8K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.4K

Area of Science:

  • Statistical analysis of time series data.
  • Information theory and complexity.
  • Statistical modeling and hypothesis testing.

Background:

  • Characterizing features induced by ordinal patterns is crucial for time series analysis.
  • Existing statistical tests are limited by the lack of knowledge on joint distributions of entropy and complexity.
  • Shannon entropy is a key measure for quantifying uncertainty in data.

Purpose of the Study:

  • To characterize the asymptotic distribution of empirical Shannon entropy for time series models.
  • To develop a general framework for building statistical tests for Shannon entropy.
  • To introduce a bilateral test for comparing Shannon entropy between two signals.

Main Methods:

  • Utilizing the central limit theorem for large time series.
  • Applying the multivariate delta method for asymptotic distribution.
  • Incorporating a third-order correction for mean value accuracy.
  • Developing a bilateral hypothesis test for comparing Shannon entropy.

Main Results:

  • The asymptotic distribution of empirical Shannon entropy was derived for models with non-extreme entropy values.
  • Accuracy and numerical stability of different correction orders were discussed.
  • A practical bilateral test for Shannon entropy was successfully developed and applied.

Conclusions:

  • The derived asymptotic distribution and developed test enhance statistical analysis of ordinal patterns.
  • The bilateral test provides a method to statistically compare entropy between time series signals.
  • The application to temperature data demonstrated the test's practical utility and sensible results.