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

Cluster Sampling Method01:20

Cluster Sampling Method

15.2K
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...
15.2K
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

283
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
283
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.3K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.3K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.8K

You might also read

Related Articles

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

Sort by
Same author

Authors' Reply: Posttrial Withdrawal Ethics in the Healthy Ageing Ecosystem for People With Dementia (HAAL) Study.

JMIR research protocols·2026
Same author

Damping behavior of adaptable shoe under torsional loading at varying angular velocities: replicating the effects on cutting maneuvers.

Scientific reports·2026
Same author

Melatonin-Loaded Sacchachitin Nanofiber Hydrogel as a Novel Non-Steroidal Platform for Atopic Dermatitis Therapy.

International journal of nanomedicine·2026
Same author

Comparison of multi-planar and sagittal-plane stepping machines for walking and balance restoration in chronic stroke: a randomized control trial (RCT).

Scientific reports·2026
Same author

The brain activation on upper extremity motor control tasks in different forces levels.

Scientific reports·2025
Same author

<i>In vitro</i> and <i>in silico</i> insights on the regulation by gonadal hormones of pituitary GnRH receptor expression in a basal teleost, the European eel.

Frontiers in endocrinology·2025
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Mar 2, 2026

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

Dissimilarity for functional data clustering based on smoothing parameter commutation.

ShengLi Tzeng1, Christian Hennig2, Yu-Fen Li1

  • 11 Department of Public Health, China Medical University, Taiwan.

Statistical Methods in Medical Research
|May 25, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for clustering functional data using smoothing splines. The approach effectively measures dissimilarity between subjects, handling various data types and offering outlier detection capabilities.

Keywords:
Clusteringdissimilarityfunctional datairregular longitudinal dataoutlierssmoothing splines

More Related Videos

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
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.9K

Related Experiment Videos

Last Updated: Mar 2, 2026

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
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
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.9K

Area of Science:

  • Statistics
  • Data Science
  • Functional Data Analysis

Background:

  • Clustering functional data is crucial for analyzing longitudinal studies.
  • Existing methods may struggle with data observed at irregular time points or be sensitive to outliers.

Purpose of the Study:

  • To propose a novel and easy-to-implement dissimilarity measure for functional data clustering.
  • To develop a method robust to irregular time points and outliers.

Main Methods:

  • Utilizing smoothing splines and smoothing parameter commutation to measure dissimilarity between subjects.
  • Estimating curves and comparing them by pairwise commutation of smoothing parameters.
  • Leveraging the relationship between smoothing parameters and signal-to-noise ratios.

Main Results:

  • The proposed method effectively clusters functional data, handling both regular and irregular time points.
  • Demonstrated robustness to outliers through simulations and a real-world application.
  • The method can be utilized for outlier detection in functional data.

Conclusions:

  • The novel dissimilarity measure based on smoothing splines offers an effective and robust approach to functional data clustering.
  • This method provides a valuable tool for analyzing longitudinal data and detecting outliers.