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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Exponential Equations with Logarithms: Problem Solving01:29

Exponential Equations with Logarithms: Problem Solving

In ecological studies, exponential models are often used to predict how populations grow over time under favorable conditions. These models assume that the growth rate is proportional to the current population, leading to continuous and compounding increases.The model expresses the population as a function of time, combining the initial population with a growth factor raised to an exponent involving the growth rate and time. To estimate how long it takes for a population to reach a specific...
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

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 the relative...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by
Same author

Extensive variation between chromosomes of North American and European hop.

Nature communications·2026
Same author

Cold Origins Limit the Establishment of Northern Temperate Plants in the Southern Hemisphere.

Systematic biology·2026
Same author

Why do Public Debates Escalate? Trigger Points and the Moral Dynamics of "Hot Politics".

The British journal of sociology·2026
Same author

Purifying selection on deleterious variants affected by the combination of subgenomes and gene expression in bread wheat.

Cell reports·2026
Same author

Author Correction: A pangenome and pantranscriptome of hexaploid oat.

Nature·2025
Same author

A pangenome and pantranscriptome of hexaploid oat.

Nature·2025

Related Experiment Video

Updated: May 21, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Parameter estimation and forecasting for multiplicative log-normal cascades.

Andrés E Leövey1, Thomas Lux

  • 1Department of Economics, University of Kiel, Kiel 24118, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|June 12, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a robust generalized method of moments (GMM) for estimating intermittency parameters in multiplicative log-normal cascade processes. The GMM improves forecasting accuracy for turbulent flow and financial market volatility.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Related Experiment Videos

Last Updated: May 21, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Physics
  • Statistical Mechanics
  • Time Series Analysis

Background:

  • Log-normal cascade processes generate intermittent time series through multiplicative random variables.
  • Traditional parameter estimation relies on fitting probability density functions, which can be challenging due to process non-stationarity.
  • Previous moment-based estimators exist but have limitations.

Purpose of the Study:

  • To develop a more rigorous generalized method of moments (GMM) for estimating cascade process parameters.
  • To improve the reliability of intermittency parameter estimation, even with uncertainty in the number of cascade steps.
  • To apply the GMM for forecasting turbulent flow and financial market volatility.

Main Methods:

  • Developed a generalized method of moments (GMM) estimation procedure.
  • Utilized the Levinson-Durbin algorithm for linear forecasting.
  • Conducted Monte Carlo simulations to compare GMM with existing methods.
  • Applied the methodology to financial market data (stocks, foreign exchange).

Main Results:

  • The GMM provides reliable intermittency parameter estimates, robust to uncertainty in cascade step numbers.
  • GMM-based forecasts of turbulent flow evolution show effectiveness.
  • Monte Carlo simulations demonstrate competitive or superior forecasting performance compared to Kiyono et al.'s method.
  • Successful estimation of intermittency and volatility forecasting on financial data.

Conclusions:

  • The GMM offers a more rigorous and reliable approach to parameter estimation in log-normal cascade processes.
  • The developed methodology enables accurate forecasting of dynamic systems, including financial markets.
  • This work advances the analysis of intermittent time series and their applications.