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

Skewness01:06

Skewness

13.4K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
13.4K
Types of Skewness01:09

Types of Skewness

13.3K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
13.3K
Spherical Coordinates01:23

Spherical Coordinates

11.5K
Spherical coordinate systems are preferred over Cartesian, polar, or cylindrical coordinates for systems with spherical symmetry. For example, to describe the surface of a sphere, Cartesian coordinates require all three coordinates. On the other hand, the spherical coordinate system requires only one parameter: the sphere's radius. As a result, the complicated mathematical calculations become simple. Spherical coordinates are used in science and engineering applications like electric and...
11.5K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

242
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,...
242
Gauss's Law: Spherical Symmetry01:26

Gauss's Law: Spherical Symmetry

8.1K
A charge distribution has spherical symmetry if the density of charge depends only on the distance from a point in space and not on the direction. In other words, if the system is rotated, it doesn't look different. For instance, if a sphere of radius R is uniformly charged with charge density ρ0, then the distribution has spherical symmetry. On the other hand, if a sphere of radius R is charged so that the top half of the sphere has a uniform charge density ρ1 and the bottom half...
8.1K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

933
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
933

You might also read

Related Articles

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

Sort by
Same author

Integrating preliminary test and Stein-type techniques to improve estimation in the time-dependent Cox model.

PloS one·2026
Same author

Supervised machine learning algorithms for classifications of gender-based violence in Somalia: a comparison of oversampling techniques.

Scientific reports·2026
Same author

Improved survival analysis with shrinkage Kibria-Lukman estimators in the Cox model: Application to lung cancer data.

Statistical methods in medical research·2026
Same author

Outdoor Walking Classification Based on Inertial Measurement Unit and Foot Pressure Sensor Data.

Sensors (Basel, Switzerland)·2026
Same author

New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application.

Journal of applied statistics·2025
Same author

A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 3: How orthopaedic research benefits from the implementation of artificial intelligence.

Journal of experimental orthopaedics·2025
Same journal

Influence of localized roadway surface obstacles on vehicular emissions under real-world urban driving conditions.

Frontiers in big data·2026
Same journal

Adaptive class-aware feature selection for high-dimensional and imbalanced multi-class network intrusion detection.

Frontiers in big data·2026
Same journal

Deep learning model to predict COPD hospital admissions based on meteorological data: a medical meteorological forecast.

Frontiers in big data·2026
Same journal

Where diverse populations gather: transit accessibility and the spatial structure of social mixing.

Frontiers in big data·2026
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
See all related articles

Related Experiment Video

Updated: Oct 2, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Coming Together of Bayesian Inference and Skew Spherical Data.

Najmeh Nakhaei Rad1,2,3, Andriette Bekker3, Mohammad Arashi3,4

  • 1Department of Mathematics and Statistics, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Frontiers in Big Data
|February 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian modeling for directional data using a novel skew-rotationally-symmetric Fisher-von Mises-Langevin distribution. The research quantifies prior impact and uses advanced sampling for analyzing circular and spherical data.

Keywords:
Fisher-von Mises-Langevin distributionGibbs samplingMCMC methodWasserstein Impact Measureskew-rotationally-symmetric distributionsslice samplerspherical data

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
08:04

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

4.5K

Related Experiment Videos

Last Updated: Oct 2, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
08:04

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

4.5K

Area of Science:

  • Statistics
  • Bayesian Inference
  • Directional Data Analysis

Background:

  • Directional data analysis is crucial in various scientific fields.
  • Existing models may not fully capture skewness and rotational symmetry.
  • Bayesian methods offer a robust framework for parameter estimation.

Purpose of the Study:

  • To introduce a new Bayesian framework for modeling skew-rotationally-symmetric directional data.
  • To develop and apply novel prior distributions for Bayesian analysis.
  • To provide practical guidance for implementing these methods.

Main Methods:

  • Utilized the skew-rotationally-symmetric Fisher-von Mises-Langevin (FvML) distribution.
  • Quantified prior impact using the Wasserstein Impact Measure (WIM).
  • Employed modified Gibbs and slice samplings for posterior computation.

Main Results:

  • Demonstrated the applicability of the FvML distribution for directional data.
  • Showcased the utility of WIM in guiding prior selection.
  • Successfully analyzed both synthetic and real-world datasets.

Conclusions:

  • The proposed Bayesian approach effectively models skew and rotational symmetry in directional data.
  • The methodology provides a valuable tool for practitioners in statistics and related fields.
  • This work opens new avenues for Bayesian analysis of complex circular and spherical data.