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

Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.6K
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...
1.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

572
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,...
572
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.7K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

10.6K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
10.6K
Divergence and Stokes' Theorems01:06

Divergence and Stokes' Theorems

4.1K
The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
4.1K
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

497
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...
497

You might also read

Related Articles

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

Sort by
Same author

Cortical activation during auditory working memory varies with hearing aid use status in age-related hearing loss.

Scientific reports·2026
Same author

Evaluating the interpretability of clinical speech AI models: Lessons from two user studies.

Computer speech & language·2026
Same author

An End-to-End Overview of Clinical Speech AI.

IEEE transactions on audio, speech, and language processing (2025)·2026
Same author

Outliers (typically) cannot cause type I errors in one-sample/paired t-tests.

PloS one·2026
Same author

Prediction of Aspiration Risk by Using Vocal Biomarkers: Machine Learning Development and Validation Study.

JMIR formative research·2026
Same author

Apple AirPods Pro 2 Live Listen as an Assistive Listening Device.

American journal of audiology·2026
Same journal

Generative Principal Component Regression via Variational Inference.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Domain Adaptive Bootstrap Aggregating.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Peak Persistence Diagrams for Shape-Based Signal Estimation.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026
Same journal

An efficient solution to Hidden Markov Models on trees with coupled branches.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
Same journal

Large-Scale Independent Vector Analysis (IVA-G) via Coresets.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
Same journal

Learnable Filters for Geometric Scattering Modules.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
See all related articles

Related Experiment Video

Updated: Mar 26, 2026

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

3.0K

Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure.

Visar Berisha, Alan Wisler, Alfred O Hero

    IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society
    |January 26, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces f-divergence measures for better error bounds in binary classification, improving accuracy for both matched and mismatched data distributions. These findings enhance machine learning model reliability.

    Keywords:
    Bayes error rateclassificationdivergence measuresdomain adaptationnon-parametric divergence estimator

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    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

    6.9K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    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

    3.0K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    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

    6.9K

    Area of Science:

    • Statistics
    • Information Theory
    • Machine Learning

    Background:

    • Information divergence functions are vital in statistics and information theory.
    • Accurate error bounds are crucial for reliable classification models.

    Purpose of the Study:

    • To demonstrate the utility of non-parametric f-divergence measures for improved error bounds in binary classification.
    • To address scenarios with both identical and mismatched training/testing data distributions.

    Main Methods:

    • Utilized non-parametric f-divergence measures to derive new bounds on classification error.
    • Designed feature selection algorithms based on these derived bounds.

    Main Results:

    • Achieved improved bounds on the minimum binary classification probability of error.
    • Demonstrated effectiveness across both matched and mismatched data distribution scenarios.
    • Validated theoretical results through practical algorithm design and evaluation.

    Conclusions:

    • Non-parametric f-divergence offers a powerful tool for enhancing classification error bounds.
    • The proposed methods show promise for improving classification performance, particularly in speech tasks.