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

Ranks01:02

Ranks

563
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...
563
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.4K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.2K
Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.4K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

561
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
561
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

380
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
380

You might also read

Related Articles

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

Sort by
Same author

SILA: a system for scientific image analysis.

Scientific reports·2022
Same author

Exclusive Neurogenic Bladder and Fecal Incontinency in an Achondroplasic Child Successfully Treated with Lumbar Foraminal Decompression.

Pediatric neurosurgery·2021
Same author

Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status.

PloS one·2018
Same author

Determinants of Vitamin D Levels in Italian Children and Adolescents: A Longitudinal Evaluation of Cholecalciferol Supplementation versus the Improvement of Factors Influencing 25(OH)D Status.

International journal of endocrinology·2014
Same author

Hydrocortisone malabsorption due to polyethylene glycols (Macrogol 3350) in a girl with congenital adrenal insufficiency.

Italian journal of pediatrics·2014
Same author

A tree-structured Markov random field model for Bayesian image segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Mar 12, 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

A Reliable Order-Statistics-Based Approximate Nearest Neighbor Search Algorithm.

Luisa Verdoliva, Davide Cozzolino, Giovanni Poggi

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 11, 2016
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a novel algorithm for efficient approximate nearest neighbor search using ordered vectors. This method excels with unstructured data, offering state-of-the-art performance in complex search tasks.

    More Related Videos

    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
    08:47

    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

    Published on: February 9, 2024

    2.1K
    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

    Related Experiment Videos

    Last Updated: Mar 12, 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
    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
    08:47

    Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

    Published on: February 9, 2024

    2.1K
    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

    Area of Science:

    • Computer Science
    • Data Science
    • Machine Learning

    Background:

    • Approximate nearest neighbor (ANN) search is crucial for large-scale data analysis.
    • Existing methods often struggle with unstructured or high-dimensional data.
    • Efficient search algorithms are needed to handle the growing volume of data.

    Purpose of the Study:

    • To develop a novel, fast algorithm for approximate nearest neighbor search.
    • To leverage the properties of ordered vectors for efficient data partitioning and search.
    • To address the challenges of searching unstructured data.

    Main Methods:

    • Data vectors are classified based on the index and sign of their largest components, partitioning space into cones.
    • Query vectors are classified, initiating search from the corresponding cone and neighboring ones.
    • The algorithm employs locality-sensitive hashing in the space of directions, using component order for hashing.

    Main Results:

    • The proposed algorithm demonstrates state-of-the-art performance on both simulated and real-world datasets.
    • It effectively handles unstructured data by exploiting statistical features from vector ordering.
    • The method provides a robust building block for more complex data processing techniques.

    Conclusions:

    • The novel ordered vector-based algorithm offers a significant advancement in fast approximate nearest neighbor search.
    • Its effectiveness with unstructured data makes it a versatile tool for various data science applications.
    • The algorithm's performance validates its potential as a foundational component for advanced data analysis.