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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

103
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...
103
Properties of Fourier series II01:21

Properties of Fourier series II

282
Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...
282
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.7K
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).
2.7K
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

732
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...
732
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
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...
89

You might also read

Related Articles

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

Sort by
Same author

Uniform in Bandwidth Consistency of the <i>L</i><sup>1</sup>-Modal Regression Estimator for High-Dimensional Data.

Entropy (Basel, Switzerland)·2026
Same author

Median Estimation with Quantile Transformations: Applications to Stratified Two-Phase Sampling.

Entropy (Basel, Switzerland)·2025
Same author

Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes.

Entropy (Basel, Switzerland)·2025
Same author

Nonparametric Expectile Shortfall Regression for Complex Functional Structure.

Entropy (Basel, Switzerland)·2024
Same author

Kolmogorov Entropy for Convergence Rate in Incomplete Functional Time Series: Application to Percentile and Cumulative Estimation in High Dimensional Data.

Entropy (Basel, Switzerland)·2023
Same author

Partial stratified ranked set sampling scheme for estimation of population mean and median.

PloS one·2023
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Scalar-on-function Mode Estimation Using Entropy And Ergodic Properties Of Functional Time Series Data.
  1. Home
  3. Scalar-on-function Mode Estimation Using Entropy And Ergodic Properties Of Functional Time Series Data.

Related Experiment Video

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.9K

Scalar-on-Function Mode Estimation Using Entropy and Ergodic Properties of Functional Time Series Data.

Mohammed B Alamari1, Fatimah A Almulhim2, Ibrahim M Almanjahie1

  • 1Department of Mathematics, College of Science, King Khalid University, Abha 62223, Saudi Arabia.

Entropy (Basel, Switzerland)
|June 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new recursive L1 estimator for conditional mode in pseudo-metric spaces, offering a robust alternative to mixing processes. The proposed method demonstrates superior performance in simulations and real-world data analysis.

Keywords:
L 1 -modal regressioncomplete consistencyconditional modeergodic datafunctional datanonparametric predictionquantile regressionrecursive estimate

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.6K
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

3.4K

Related Experiment Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.9K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.6K
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

3.4K

Area of Science:

  • Statistics
  • Time Series Analysis
  • Functional Data Analysis

Background:

  • Standard estimators for conditional mode often rely on mixing processes, which can be mathematically complex.
  • Ergodicity offers a more tractable assumption, characterized by Kolmogorov-Sinai entropy, reflecting process dynamics and fluctuations.
  • Functional time series (fts) present unique challenges due to their complex mathematical properties.

Purpose of the Study:

  • To develop and analyze a novel recursive L1 estimator for the conditional mode.
  • To investigate the estimator's properties when the input variable is in a pseudo-metric space.
  • To provide a robust alternative to existing methods using an ergodicity assumption.

Main Methods:

  • Construction of a recursive L1 estimator under an ergodicity assumption for functional time series.
  • Derivation of asymptotic properties, including convergence rate and Borel-Cantelli (BC) consistency.
  • Specialization of convergence rates to independent cases, kernel methods, and vector-valued scenarios.

    • Main Results:

    • The proposed recursive L1 estimator is shown to be asymptotically consistent (BC consistent).
    • Specific convergence rates are derived for various functional time series settings.
    • Numerical experiments confirm the estimator's superiority over existing methods.

    Conclusions:

    • The novel recursive L1 estimator provides a robust and effective approach for conditional mode estimation in pseudo-metric spaces.
    • Ergodicity offers a practical and mathematically sound alternative to mixing assumptions in functional time series analysis.
    • The estimator's strong performance on simulated and real data validates its utility.