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

What is a Mode?01:07

What is a Mode?

28.2K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
28.2K
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

477
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
477
Skewness01:06

Skewness

20.4K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
20.4K
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

1.3K
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...
1.3K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.3K
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...
5.3K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

10.4K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
10.4K

You might also read

Related Articles

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

Sort by
Same author

Graph Frequency-Domain Factor Modeling.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Cross-Spectral Analysis of Bivariate Graph Signals.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Probabilistic Principal Curves on Riemannian Manifolds.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform.

Journal of classification·2023
Same author

Dynamic principal component analysis with missing values.

Journal of applied statistics·2022
Same author

Spherical Principal Curves.

IEEE transactions on pattern analysis and machine intelligence·2020
Same journal

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors.

SpringerPlus·2017
Same journal

Erratum to: Implication of Paris Agreement in the context of long-term climate mitigation goals.

SpringerPlus·2017
Same journal

Erratum to: Associations between adherence, depressive symptoms and health-related quality of life in young adults with cystic fibrosis.

SpringerPlus·2017
Same journal

Erratum to: Numerical method to compute acoustic scattering effect of a moving source.

SpringerPlus·2017
Same journal

Identifying appropriate protected areas for endangered fern species under climate change.

SpringerPlus·2017
Same journal

An Algorithm to detect balancing of iterated line sigraph.

SpringerPlus·2017
See all related articles

Related Experiment Video

Updated: Mar 10, 2026

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

11.2K

Empirical mode decomposition with missing values.

Donghoh Kim1, Hee-Seok Oh2

  • 1Department of Applied Mathematics, Sejong University, Seoul, 05006 Korea.

Springerplus
|December 13, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Empirical Mode Decomposition (EMD) method to effectively handle missing data. The new approach ensures stable signal decomposition results, improving analysis of nonlinear and nonstationary signals.

Keywords:
Empirical mode decompositionImputationMissingMultiscale methodSelf-consistency

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

646

Related Experiment Videos

Last Updated: Mar 10, 2026

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

11.2K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

646

Area of Science:

  • Signal Processing
  • Data Analysis

Background:

  • Empirical Mode Decomposition (EMD) is a technique for analyzing nonlinear and nonstationary signals.
  • Conventional EMD methods struggle with datasets containing missing values, limiting their applicability.

Purpose of the Study:

  • To develop an improved Empirical Mode Decomposition (EMD) method capable of handling missing data.
  • To enhance the stability and effectiveness of signal decomposition in the presence of data gaps.

Main Methods:

  • A novel approach combining Empirical Mode Decomposition (EMD) with the self-consistency concept for data imputation.
  • Development of a modified EMD procedure to address missing data challenges.

Main Results:

  • The proposed method demonstrates effective imputation of missing data using self-consistency.
  • Simulation studies and image analysis confirm the stability and effectiveness of the modified EMD procedure.
  • Substantially improved decomposition results compared to conventional EMD in the presence of missing data.

Conclusions:

  • The modified EMD procedure offers a robust solution for signal decomposition with missing data.
  • This advancement enhances the utility of EMD in various scientific and engineering applications where data is often incomplete.