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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.2K
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...
4.2K
Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.1K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

637
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
637
Modified Boxplots00:57

Modified Boxplots

11.4K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
11.4K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
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...
7.1K
Censoring Survival Data01:09

Censoring Survival Data

599
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
599

You might also read

Related Articles

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

Sort by
Same author

Psychotherapy for PTSD - a scoping review of how change during treatment has been studied.

European journal of psychotraumatology·2026
Same author

Genomic features of clonal hematopoiesis-associated genes in primary and CAR T-cell-related secondary T-cell malignancies.

Blood neoplasia·2026
Same author

Secondary T-cell lymphomas after chimeric antigen receptor T-cell therapy in dermatology.

Journal of the American Academy of Dermatology·2026
Same author

Complex relationships between coping strategies, posttraumatic stress, anxiety, and depression in Colombians exposed to armed conflict violence.

Psychology, health & medicine·2026
Same author

A Non-Parametric Approach to Modeling Accelerated Longitudinal Designs.

Multivariate behavioral research·2026
Same author

Game, Set, and Match: A Scoping Review of Matching Characteristics for Control and Intervention Groups in Adaptive Behavioral Interventions for Physical Activity or Healthy Eating Designs for Populations with Overweight and Obesity.

Behavioral medicine (Washington, D.C.)·2026

Related Experiment Video

Updated: Feb 22, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Penalized Subgrouping of Heterogeneous Time Series.

Christopher M Crawford1, Jonathan J Park2, Sy-Miin Chow3

  • 1The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Multivariate Behavioral Research
|February 20, 2026
PubMed
Summary

This study extends the multi-VAR framework to identify subgroup dynamics in longitudinal data. The new method effectively models shared patterns across individuals, improving analysis of complex processes.

Keywords:
Time series analysisclusteringheterogeneityregularization

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.8K

Related Experiment Videos

Last Updated: Feb 22, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.8K

Area of Science:

  • Social Sciences
  • Behavioral Sciences
  • Health Sciences

Background:

  • Intensive longitudinal data is increasingly available in social, behavioral, and health sciences.
  • Modeling persistent heterogeneity in dynamic processes remains a challenge.
  • The multi-VAR framework addresses heterogeneous dynamics in multiple-subject time series.

Purpose of the Study:

  • To extend the multi-VAR framework for identifying subgroup-specific dynamics.
  • To allow for penalized estimation of shared patterns across subsets of individuals.
  • To evaluate the performance of the subgrouping extension.

Main Methods:

  • Extension of the multi-vector autoregressive (multi-VAR) framework.
  • Decomposition of individual-level transition matrices into common, unique, and subgroup-specific dynamics.
  • Structured penalization for parameter estimation.

Main Results:

  • The proposed subgrouping extension successfully identifies subgroup-specific dynamics.
  • Performance evaluated through simulation and empirical application.
  • Comparison with alternative subgrouping methods for multivariate time series.

Conclusions:

  • The extended multi-VAR framework provides a robust method for analyzing subgroup dynamics in longitudinal data.
  • This approach enhances the understanding of shared patterns within subsets of individuals.
  • Offers improved modeling capabilities for complex, heterogeneous processes.