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

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.0K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.2K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

8.3K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
8.3K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

6.3K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
6.3K
Introduction to Test of Independence01:21

Introduction to Test of Independence

3.0K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
3.0K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.6K
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...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Anomalous Saturation of CO Adsorption at 26% on Cu(111) Governed by Nanometer-Scale Substrate-Mediated Interactions.

Journal of the American Chemical Society·2025
Same author

Opportunities and challenges of diffusion models for generative AI.

National science review·2024
Same author

Are Latent Factor Regression and Sparse Regression Adequate?

Journal of the American Statistical Association·2024
Same author

Understanding Implicit Regularization in Over-Parameterized Single Index Model.

Journal of the American Statistical Association·2024
Same author

Communication-Efficient Accurate Statistical Estimation.

Journal of the American Statistical Association·2023
Same author

Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs.

Journal of the American Statistical Association·2023

Related Experiment Video

Updated: Feb 20, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.3K

Estimation of the false discovery proportion with unknown dependence.

Jianqing Fan1, Xu Han2

  • 1Department of Operations Research & Financial Engineering, Princeton University, Princeton, New Jersey 08544, U.S.A. and School of Data Science, Fudan University, Shanghai, China.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|October 24, 2017
PubMed
Summary

This study develops a framework for accurately approximating the false discovery proportion (FDP) when the covariance matrix of test statistics is unknown. It addresses challenges from estimating dependence structures, improving multiple testing in scientific research.

Keywords:
Large-scale multiple testingapproximate factor modeldependent test statisticsfalse discovery proportionunknown covariance matrix

More Related Videos

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

14.0K
Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.0K

Related Experiment Videos

Last Updated: Feb 20, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.3K
Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

14.0K
Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

19.0K

Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics
  • Neuroscience

Background:

  • Large-scale multiple testing is common in scientific research, often involving correlated test statistics.
  • Accurate approximation of the false discovery proportion (FDP) is crucial for reliable results.
  • Existing methods by Fan, Han & Gu (2012) assume a known covariance matrix, which is often impractical.

Purpose of the Study:

  • To theoretically investigate the impact of unknown dependence on FDP approximation.
  • To establish a general framework for well-approximating FDP even with estimated covariance matrices.
  • To address challenges arising from estimating eigenvalues, eigenvectors, and marginal variances.

Main Methods:

  • Developed general requirements for eigenvalue and eigenvector estimates to ensure good FDP approximation.
  • Identified conditions on covariance matrix structures (e.g., banded, sparse, conditional sparse precision matrices) that meet these requirements.
  • Illustrated the framework with data from an approximate factor model and generalized to non-multivariate normal distributions.

Main Results:

  • Provided a theoretical framework for FDP approximation with unknown covariance matrices.
  • Demonstrated that specific covariance structures (banded, sparse, conditional sparse precision) facilitate accurate FDP approximation.
  • Achieved a good FDP approximation by exploiting the dependence structure within an approximate factor model.

Conclusions:

  • The proposed framework effectively handles the impact of unknown dependence in large-scale multiple testing.
  • The method offers improved FDP approximation accuracy, crucial for robust scientific conclusions.
  • Validated through simulations and real-data applications, highlighting practical utility.