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

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

1.4K
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...
1.4K
The Thermodynamics of Mixing01:28

The Thermodynamics of Mixing

110
Mixing is a fascinating phenomenon in thermodynamics, particularly when considering the Gibbs energy of a mixture at constant temperature and pressure. This energy, denoted as G, tends to decrease during spontaneous mixing processes, offering insights into the composition changes that occur.Imagine two ideal gases, initially separated in different containers, with amounts nA and nB, respectively, both at a temperature T and pressure p. The chemical potentials of these gases have their 'pure'...
110
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.5K
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

2.2K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
2.2K
Spin–Spin Coupling Constant: Overview01:08

Spin–Spin Coupling Constant: Overview

1.6K
In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
Qualitatively, any spin plus-half nucleus polarizes the spins of its electrons to the minus-half state. Consequently, the paired electron in the hydrogen–carbon bond must...
1.6K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

77.5K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
77.5K

You might also read

Related Articles

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

Sort by
Same author

From wastewater to infection estimates: Incident COVID-19 infections during Omicron in the U.S.

Epidemics·2026
Same author

Incident COVID-19 infections before Omicron in the U.S.

Epidemics·2025
Same author

Medial joint line tenderness is an indicator for meniscal injuries in dogs.

The Veterinary record·2024
Same author

Smooth multi-period forecasting with application to prediction of COVID-19 cases.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2024
Same author

rtestim: Time-varying reproduction number estimation with trend filtering.

PLoS computational biology·2024
Same author

Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Nature communications·2024
Same journal

Entropic Graph-based Posterior Regularization.

JMLR workshop and conference proceedings·2024
Same journal

Uncovering Voice Misuse Using Symbolic Mismatch.

JMLR workshop and conference proceedings·2021
Same journal

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data.

JMLR workshop and conference proceedings·2018
Same journal

Anytime Exploration for Multi-armed Bandits using Confidence Information.

JMLR workshop and conference proceedings·2018
Same journal

Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain.

JMLR workshop and conference proceedings·2017
Same journal

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.

JMLR workshop and conference proceedings·2017
See all related articles

Related Experiment Video

Updated: Apr 5, 2026

Quantifying Mixing using Magnetic Resonance Imaging
07:33

Quantifying Mixing using Magnetic Resonance Imaging

Published on: January 25, 2012

11.5K

Estimating beta-mixing coefficients.

Daniel J McDonald1, Cosma Rohilla Shalizi2, Mark Schervish3

  • 1Department of Statististics, Carnegie Mellon University, Pittsburgh, PA 15213, danielmc@stat.cmu.edu.

JMLR Workshop and Conference Proceedings
|August 18, 2015
PubMed
Summary
This summary is machine-generated.

Statistical learning for time series relies on untested mixing assumptions. This study introduces a novel estimator for beta-mixing rates from data, demonstrating its L1-risk consistency for stationary time series.

More Related Videos

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

1.2K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K

Related Experiment Videos

Last Updated: Apr 5, 2026

Quantifying Mixing using Magnetic Resonance Imaging
07:33

Quantifying Mixing using Magnetic Resonance Imaging

Published on: January 25, 2012

11.5K
Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

1.2K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K

Area of Science:

  • Statistics
  • Time Series Analysis
  • Machine Learning

Background:

  • Statistical learning literature for time series commonly assumes asymptotic independence or "mixing" of the data-generating process.
  • These critical mixing assumptions are frequently untested in practice.
  • Current methodologies lack ways to estimate mixing rates directly from observed data.

Purpose of the Study:

  • To address the gap in testing and estimating mixing properties in time series data.
  • To propose a novel method for quantifying the mixing rate of a data-generating process.
  • To validate the proposed method's performance and theoretical guarantees.

Main Methods:

  • Development of an estimator for the beta-mixing rate.
  • The estimator is designed to work with a single stationary sample path.
  • Theoretical analysis to establish the estimator's consistency properties.

Main Results:

  • A novel estimator for the beta-mixing rate has been successfully developed.
  • The proposed estimator is shown to be L1-risk consistent.
  • This provides a data-driven method for assessing time series mixing properties.

Conclusions:

  • The study provides the first data-driven estimator for beta-mixing rates in time series.
  • The L1-risk consistency guarantees the reliability of the proposed estimation method.
  • This work enables empirical validation of mixing assumptions in statistical learning for time series.