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

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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

491
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,...
491
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

709
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
709
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

759
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
759
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

921
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
921
Social Exchange Theory02:06

Social Exchange Theory

40.8K
We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
40.8K
Social Scripts02:10

Social Scripts

10.3K
People tend to know what behavior is expected of them in specific, familiar settings. A script is a person’s knowledge about the sequence of events expected in a specific setting (Schank & Abelson, 1977). Essentially, scripts are a particular kind of schema, one containing default values for the features within an event. In the restaurant example, the script's features include the props (e.g., tables, menu, food, and money), the roles to be played (e.g., customer and waiter),...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Systematic estimates of global causes of neonatal and under 5 mortality in 2000-24: secondary data analysis using bayesian multinomial logistic regression.

BMJ (Clinical research ed.)·2026
Same author

What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research.

Statistics in medicine·2025
Same author

Estrogen Enhances SK Channel Activity to Limit Hippocampal Arteriole Constriction.

Circulation research·2025
Same author

Fetal Body Composition in Twins and Singletons.

JAMA pediatrics·2025
Same author

Fetal Body Composition and Organ Volume Trajectories in Association With Maternal Perceived Stress or Depressive Symptoms in the Fetal 3D Study.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2025
Same author

Relationship between gestational weight gain with fetal body composition and organ volumes in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Dimensional Study: a prospective pregnancy cohort.

The American journal of clinical nutrition·2025
Same journal

Mode hunting through active information.

Applied stochastic models in business and industry·2021
Same journal

Integrative Interaction Analysis using Threshold Gradient Directed Regularization.

Applied stochastic models in business and industry·2020
Same journal

Copula-based robust optimal block designs.

Applied stochastic models in business and industry·2020
Same journal

Weak signals in high-dimension regression: detection, estimation and prediction.

Applied stochastic models in business and industry·2019
Same journal

Clinical Trial Design as a Decision Problem.

Applied stochastic models in business and industry·2017
Same journal

Maximum likelihood estimation for stochastic volatility in mean models with heavy-tailed distributions.

Applied stochastic models in business and industry·2017
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Assessment of Social Interaction Behaviors
06:41

Assessment of Social Interaction Behaviors

Published on: February 25, 2011

95.3K

Inferring social structure from continuous-time interaction data.

Wesley Lee1, Bailey K Fosdick2, Tyler H McCormick1

  • 1University of Washington.

Applied Stochastic Models in Business and Industry
|July 3, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing relational event data, distinguishing between fleeting and persistent social connections. It focuses on consistent behavioral patterns to reveal underlying social network structures.

Keywords:
continuous time networklatent networkpoint processrelational event data

More Related Videos

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
10:45

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

12.2K
Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

1.6K

Related Experiment Videos

Last Updated: Feb 8, 2026

Assessment of Social Interaction Behaviors
06:41

Assessment of Social Interaction Behaviors

Published on: February 25, 2011

95.3K
Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
10:45

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

Published on: July 6, 2011

12.2K
Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
08:08

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

1.6K

Area of Science:

  • Social Network Analysis
  • Statistical Modeling
  • Behavioral Ecology

Background:

  • Relational event data capture interactions between actors over time.
  • Current models often focus on interaction contagion and latent variables.
  • High-resolution temporal data necessitates advanced analytical approaches.

Purpose of the Study:

  • To propose an alternative to existing temporal-relational point process models.
  • To differentiate between spurious and persistent connections in relational event data.
  • To identify underlying social network structures using consistent behavioral deviations.

Main Methods:

  • Characterizing interactions as spurious or persistent.
  • Modeling continuous-time event data using a novel approach.
  • Analyzing latent social network structures.

Main Results:

  • Consistent deviations from expected behavior are key indicators of stable relationships.
  • The proposed method can uncover latent social network structures.
  • The approach is applicable across different domains, including human and animal behavior.

Conclusions:

  • This research offers a new perspective on understanding social relationships from relational event data.
  • The findings highlight the importance of behavioral consistency over interaction frequency.
  • The method provides a valuable tool for analyzing complex relational dynamics.