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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.3K
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...
6.3K
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

313
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
313
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.0K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.0K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

120
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
120
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

454
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...
454

You might also read

Related Articles

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

Sort by
Same author

Cytoskeleton-Inspired Mechanically Interlocked Catenane Framework Enabling Robust yet Dynamic Polymer Networks.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Exogenous sorbitol-chelated calcium mitigates toxicity of cadmium in peanut seedlings through physiological, biochemical, and transcriptomic regulation.

Frontiers in plant science·2026
Same author

Understanding the Effects of Projectors in Knowledge Distillation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Carbon dots promote cotton growth and development by enhancing photosynthesis and antioxidant enzyme activities.

BMC plant biology·2026
Same author

Dibenzocyclooctyne Conjugation Enhances Antigen Cross-Presentation and T-Cell Killing for Potent Cancer Vaccines.

Journal of the American Chemical Society·2026
Same author

Double sulfuration promotes the activation of peroxydisulfate by Fe-based N/S co-doped biochar composites to degrade sulfapyridine: Synergistic effect and degradation mechanism.

Environmental research·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K

Inference in High-Dimensional Online Changepoint Detection.

Yudong Chen1,2, Tengyao Wang2, Richard J Samworth1

  • 1Statistical Laboratory, University of Cambridge, Cambridge, UK.

Journal of the American Statistical Association
|July 8, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods for detecting changes in high-dimensional data, providing reliable confidence intervals for changepoints and identifying changed coordinates. The new online algorithm ensures accurate change detection with controlled errors.

Keywords:
Confidence intervalSequential methodSparsitySupport estimate

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.0K

Related Experiment Videos

Last Updated: Jun 21, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.3K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.0K

Area of Science:

  • Statistics
  • High-Dimensional Data Analysis
  • Change-Point Detection

Background:

  • Sequential detection of changes in high-dimensional mean vectors presents significant inferential challenges.
  • Accurate identification of both the timing and location of changes is crucial for many applications.

Purpose of the Study:

  • To develop methods for estimating a confidence interval for the changepoint in high-dimensional data.
  • To estimate the set of coordinate indices where the mean vector changes.
  • To propose an online algorithm addressing these inferential challenges.

Main Methods:

  • Introduction of an online algorithm for sequential change-point detection.
  • Development of a confidence interval for the changepoint with guaranteed nominal coverage.
  • Estimation of the coordinate set with control over false positives and false negatives.

Main Results:

  • The proposed algorithm yields a confidence interval whose length is asymptotically comparable to the detection delay.
  • The support estimate effectively controls both false negatives and false positives.
  • Theoretical guarantees on coverage and error control are established.

Conclusions:

  • The developed methodology provides a robust solution for inferential challenges in high-dimensional sequential change detection.
  • The approach is validated through simulations and demonstrated on real-world U.S. excess deaths data.
  • This work offers a significant advancement in statistical methods for analyzing dynamic high-dimensional datasets.