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

Modeling with Differential Equations01:25

Modeling with Differential Equations

91
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
91
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

261
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
261
Structure of Benzene: Kekulé Model01:07

Structure of Benzene: Kekulé Model

12.0K
In 1865, August Kekule suggested the structure of benzene according to the structural theory of organic chemistry based on the three assertions—formula of benzene is C6H6, all the hydrogens of benzene are equivalent, and each carbon must have four bonds due to its tetravalency.
He proposed that benzene has a cyclic structure of six carbon atoms attached to one hydrogen atom each, with three alternating pi bonds.
12.0K
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

12.7K
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
12.7K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

59.2K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
59.2K
Average Acceleration01:30

Average Acceleration

14.2K
The importance of understanding acceleration spans our day-to-day experiences, as well as the vast reaches of outer space and the tiny world of subatomic physics. In everyday conversation, to accelerate means to speed up. For instance, we are familiar with the acceleration of our car; the harder we apply our foot to the gas pedal, the faster we accelerate. The greater the acceleration, the greater the change in velocity over a given time. Acceleration is widely seen in experimental physics. In...
14.2K

You might also read

Related Articles

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

Sort by
Same author

Impairment of hippocampal gamma oscillations, mitochondria and neurovascular function in CADASIL.

Brain : a journal of neurology·2026
Same author

Recent Advances and Retrospective Review in Bioinspired Structures for Fog Water Collection.

Biomimetics (Basel, Switzerland)·2025
Same author

Fabrication of Stretchable Piezoelectric Sensor with a Kirigami Design for Heart Sound Monitoring.

Sensors (Basel, Switzerland)·2025
Same author

Three-Dimensional Manipulation of Micromodules Using Twin Optothermally Actuated Bubble Robots.

Micromachines·2024
Same author

Fast estimation of generalized linear latent variable models for performance and process data with ordinal, continuous, and count observed variables.

The British journal of mathematical and statistical psychology·2024
Same author

Insulator Defect Detection Based on ML-YOLOv5 Algorithm.

Sensors (Basel, Switzerland)·2024
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Feb 9, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.6K

Frequentist Model Averaging in Structural Equation Modelling.

Shaobo Jin1, Sebastian Ankargren2

  • 1Department of Statistics, Uppsala University, Uppsala, Sweden. shaobo.jin@statistik.uu.se.

Psychometrika
|June 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model averaging technique in frequentist statistics, improving upon traditional model selection. The new approach offers robust performance and better statistical inference for structural equation modeling applications.

Keywords:
coverage probabilitygoodness-of-fitlocal asymptoticmodel selectionpost-selection inference

More Related Videos

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

1.1K
Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
07:29

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Published on: December 1, 2011

42.0K

Related Experiment Videos

Last Updated: Feb 9, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.6K
Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

1.1K
Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
07:29

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

Published on: December 1, 2011

42.0K

Area of Science:

  • Statistics
  • Econometrics
  • Psychometrics

Background:

  • Model selection is crucial in structural equation modeling (SEM).
  • Traditional methods can introduce unaccounted randomness, affecting inference.
  • Existing approaches often face limitations in balancing model fit and generalizability.

Purpose of the Study:

  • To propose a frequentist model averaging technique for SEM.
  • To address limitations of traditional model selection methods.
  • To provide a statistically sound alternative that accounts for model uncertainty.

Main Methods:

  • Developed a model averaging method within the frequentist framework.
  • Proposed valid confidence intervals and a test statistic for model averaging.
  • Utilized simulation studies to compare performance against model selection.

Main Results:

  • The proposed model averaging method demonstrated robust mean-squared error.
  • Achieved better coverage probability compared to traditional model selection.
  • Showcased superior goodness-of-fit testing performance in simulations.

Conclusions:

  • Model averaging offers a valuable compromise between selecting a single best model and using the full model.
  • The technique provides more reliable statistical inference by acknowledging uncertainty across candidate models.
  • This approach enhances the practical application of structural equation modeling.