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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K
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

587
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...
587
Outliers and Influential Points01:08

Outliers and Influential Points

4.1K
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...
4.1K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

158
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,...
158
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
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...
6.3K

You might also read

Related Articles

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

Sort by
Same author

The subpleural pulmonary microvasculature in newborn yak (Bos grunniens).

Veterinary research communications·2008
Same author

Experimental confirmation of potential swept source optical coherence tomography performance limitations.

Applied optics·2008
Same author

A germin-like protein gene family functions as a complex quantitative trait locus conferring broad-spectrum disease resistance in rice.

Plant physiology·2008
Same author

[Spatial and temporal changes of palatal cell proliferation and cell apoptosis of retinoic acid induced mouse cleft palate in different embryonic stages].

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology·2008
Same author

Identification of an Atlantic salmon IFN multigene cluster encoding three IFN subtypes with very different expression properties.

Developmental and comparative immunology·2008
Same author

Non-Gaussian statistics and superdiffusion in a driven-dissipative dusty plasma.

Physical review. E, Statistical, nonlinear, and soft matter physics·2008
Same journal

Semiparametric regression methods for temporal processes subject to multiple sources of censoring.

The Canadian journal of statistics = Revue canadienne de statistique·2026
Same journal

Robust causal inference for point exposures with missing confounders.

The Canadian journal of statistics = Revue canadienne de statistique·2025
Same journal

Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models.

The Canadian journal of statistics = Revue canadienne de statistique·2025
Same journal

Variable selection in modelling clustered data via within-cluster resampling.

The Canadian journal of statistics = Revue canadienne de statistique·2025
Same journal

Robust Estimation of Loss-Based Measures of Model Performance under Covariate Shift.

The Canadian journal of statistics = Revue canadienne de statistique·2024
Same journal

Optimal multiwave validation of secondary use data with outcome and exposure misclassification.

The Canadian journal of statistics = Revue canadienne de statistique·2024
See all related articles

Related Experiment Video

Updated: Jul 26, 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

11.4K

Efficient multiple change point detection for high-dimensional generalized linear models.

Xianru Wang1, Bin Liu1, Xinsheng Zhang1

  • 1Department of Statistics and Data Science, School of Management at Fudan University, Shanghai, China.

The Canadian Journal of Statistics = Revue Canadienne De Statistique
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms for detecting multiple change points in high-dimensional generalized linear models. The methods accurately identify changes in data, even with complex structures and an unknown number of shifts.

Keywords:
Binary segmentationDynamic programmingGeneralized linear modelsHigh dimensions

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

12.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Related Experiment Videos

Last Updated: Jul 26, 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

11.4K
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

12.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • High-dimensional data analysis presents challenges in accurately detecting changes.
  • Generalized linear models are widely used but require robust change point detection methods.

Purpose of the Study:

  • To develop and evaluate novel algorithms for multiple change point detection in high-dimensional generalized linear models.
  • To address scenarios where the covariate dimension grows exponentially with sample size.
  • To automatically detect an unknown number of change points.

Main Methods:

  • Utilized dynamic programming and binary segmentation techniques to create two primary algorithms.
  • Developed a more efficient algorithm specifically for single change point detection.
  • Analyzed theoretical properties including estimation consistency and asymptotic distributions.

Main Results:

  • Proposed algorithms demonstrate consistency in estimating the number and locations of change points.
  • Theoretical analysis confirms consistency and asymptotic distributions for regression coefficients.
  • Simulations and real-world data application (Alzheimer's Disease Neuroimaging Initiative) show competitive performance.

Conclusions:

  • The developed algorithms offer effective solutions for multiple change point detection in high-dimensional generalized linear models.
  • Methods are flexible, adaptable to various data generation mechanisms, and computationally efficient.
  • The approach is validated through simulations and a significant biomedical dataset.