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

Ranks01:02

Ranks

514
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
514
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.5K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.5K
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

781
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
781
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

512
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
512
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.1K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.1K
What are Estimates?01:06

What are Estimates?

8.9K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging.

PloS one·2026
Same author

Accurate and efficient data-driven psychiatric assessment using machine learning.

BMC medical informatics and decision making·2026
Same author

3D Neuromodulation in Neural Organoids with Shell MEAs.

Advanced healthcare materials·2026
Same author

Toward a science of prospective learning.

Neuron·2025
Same author

Is Pearson's correlation coefficient enough for functional connectivity in fMRI?

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine.

Proceedings of the National Academy of Sciences of the United States of America·2025

Related Experiment Video

Updated: Feb 15, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

16.6K

An M-estimator for reduced-rank system identification.

Shaojie Chen1, Kai Liu2, Yuguang Yang3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore 21205, USA.

Pattern Recognition Letters
|February 3, 2018
PubMed
Summary

We developed a new method, MR-SID, to analyze complex, high-dimensional time-series data. This approach efficiently estimates parameters and predicts future values, overcoming limitations of existing techniques for fields like neuroscience.

Keywords:
High dimensionImage processingParameter estimationState-space modelTime series analysis

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

972
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Related Experiment Videos

Last Updated: Feb 15, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

16.6K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

972
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Area of Science:

  • Computational neuroscience
  • Statistical modeling
  • Time-series analysis

Background:

  • High-dimensional time-series data are increasingly common in fields like neuroscience.
  • Existing statistical models struggle with the computational and statistical challenges of this data.
  • Accurate parameter estimation and time-series prediction are crucial for data analysis.

Purpose of the Study:

  • To address the limitations of current methods for analyzing high-dimensional time-series data.
  • To introduce a computationally efficient and numerically stable M-estimator for Reduced-rank System IDentification (MR-SID).
  • To enable the analysis of large time-series datasets using standard statistical methods.

Main Methods:

  • Utilizing low-rank approximations to handle data dimensionality.
  • Incorporating both ℓ1 and ℓ2 penalties for robust estimation.
  • Employing numerical linear algebra techniques for computational efficiency and stability.

Main Results:

  • MR-SID demonstrated computational efficiency and numerical stability.
  • The method accurately estimated spatial filters, connectivity graphs, and time-courses from functional magnetic resonance imaging (fMRI) data.
  • Simulations and real-world data examples confirmed the approach's utility across various problems.

Conclusions:

  • MR-SID provides a powerful tool for analyzing high-dimensional time-series data.
  • This method facilitates the application of standard analysis techniques to large datasets.
  • The approach paves the way for future extensions, including non-linear and non-Gaussian state-space models.