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

F Distribution01:19

F Distribution

10.9K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
10.9K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

4.2K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
4.2K
Upsampling01:22

Upsampling

696
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
696
Shock Waves01:16

Shock Waves

2.7K
While deriving the Doppler formula for the observed frequency of a sound wave, it is assumed that the speed of sound in the medium is greater than the source's speed through it. When this condition is breached, a shock wave occurs.
When the source's speed approaches the speed of sound, constructive interference between successive wavefronts emitted by the source occurs immediately behind it. Initially, scientists believed that this constructive interference would result in such high...
2.7K
Aliasing01:18

Aliasing

776
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
776
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

9.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
9.0K

You might also read

Related Articles

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

Sort by
Same author

A simple neuronal model with intrinsic saturation of the firing frequency.

Bio Systems·2022
Same author

Fano Factor: A Potentially Useful Information.

Frontiers in computational neuroscience·2020
Same author

Statistics of inverse interspike intervals: The instantaneous firing rate revisited.

Chaos (Woodbury, N.Y.)·2018
Same author

The Jacobi diffusion process as a neuronal model.

Chaos (Woodbury, N.Y.)·2018
Same author

A single spike deteriorates synaptic conductance estimation.

Bio Systems·2017
Same author

Accuracy of rate coding: When shorter time window and higher spontaneous activity help.

Physical review. E·2017
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
07:47

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

7.6K

Shot-noise Fano factor.

Kamil Rajdl1, Petr Lansky1

  • 1Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic and Institute of Physiology, Academy of Sciences of the Czech Republic, Videnska 1083, 142 20 Prague 4, Czech Republic.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 15, 2015
PubMed
Summary
This summary is machine-generated.

A new variability measure for event timing, inspired by the Fano factor, is introduced. This generalized measure, based on shot-noise processes, offers a novel way to analyze event sequences.

More Related Videos

Fluorescence Imaging with One-nanometer Accuracy FIONA
11:56

Fluorescence Imaging with One-nanometer Accuracy FIONA

Published on: September 26, 2014

18.3K
Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
04:54

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging

Published on: June 16, 2023

4.0K

Related Experiment Videos

Last Updated: Mar 29, 2026

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
07:47

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

7.6K
Fluorescence Imaging with One-nanometer Accuracy FIONA
11:56

Fluorescence Imaging with One-nanometer Accuracy FIONA

Published on: September 26, 2014

18.3K
Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
04:54

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging

Published on: June 16, 2023

4.0K

Area of Science:

  • * Physics
  • * Statistical Analysis
  • * Signal Processing

Background:

  • * The Fano factor is a common measure for event variability.
  • * Analyzing the timing of uniform events requires robust statistical methods.
  • * Shot-noise processes are frequently used to model event sequences.

Purpose of the Study:

  • * To propose a novel variability measure for uniform event timing.
  • * To generalize the Fano factor using a shot-noise process.
  • * To analyze the properties and applications of this new measure.

Main Methods:

  • * Developed a variability measure inspired by the Fano factor.
  • * Utilized a shot-noise process and an equilibrium renewal process model.
  • * Derived formulas for the measure's behavior with a general response function, focusing on exponential decay.

Main Results:

  • * Introduced a generalized variability measure for event timing.
  • * Derived formulas describing the measure's behavior under specific assumptions.
  • * Demonstrated the measure's applicability to shot-noise processes with exponential decay.

Conclusions:

  • * The proposed measure offers a new tool for analyzing event timing variability.
  • * It extends the Fano factor by incorporating time-weighted event influences.
  • * The study provides a theoretical framework and formulas for its application.