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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
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).
2.5K
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

399
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...
399
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

150
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...
150
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
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...
1.5K
Poisson's Ratio01:23

Poisson's Ratio

382
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
382

You might also read

Related Articles

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

Sort by
Same author

Selenium accumulated exposure aggravates the progression of type 2 diabetes mellitus via activating the IRF2/Caspase-4/GSDMD pyroptosis pathway.

The Journal of nutritional biochemistry·2026
Same author

Communication-efficient estimation and inference for high-dimensional quantile regression based on smoothed decorrelated score.

Statistics in medicine·2022
Same author

Estimation and inference for multikink expectile regression with longitudinal data.

Statistics in medicine·2021
See all related articles

Related Experiment Video

Updated: Jun 9, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K

Optimal Poisson subsampling decorrelated score for high-dimensional generalized linear models.

Junhao Shan1, Lei Wang1

  • 1School of Statistics and Data Science, KLMDASR, LEBPS and LPMC, Nankai University, Tianjin, People's Republic of China.

Journal of Applied Statistics
|October 23, 2024
PubMed
Summary

This study introduces an optimal Poisson subsampling method for high-dimensional generalized linear models (GLMs) to improve parameter estimation and inference. The proposed technique enhances efficiency and mitigates issues with large datasets.

Keywords:
A-optimalityPoisson subsamplinghigh-dimensional inferencemassive datasubsampling decorrelated score

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.2K

Related Experiment Videos

Last Updated: Jun 9, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.2K

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • High-dimensional generalized linear models (GLMs) face challenges with massive datasets.
  • Efficient estimation and inference are crucial for these models.

Purpose of the Study:

  • To develop a unified optimal Poisson subsampling scheme for high-dimensional GLMs.
  • To improve estimation and inference for low-dimensional parameter partitions.

Main Methods:

  • A Poisson subsampling decorrelated score function is proposed.
  • Consistency and asymptotic normality of the subsample estimator are proven.
  • A general optimal subsampling criterion (A- and L-optimality) is formulated.

Main Results:

  • The method mitigates the impact of inaccurate nuisance parameter estimation.
  • The subsample estimator demonstrates consistency and asymptotic normality.
  • Improved estimation efficiency is achieved through the optimal subsampling criterion.

Conclusions:

  • The proposed Poisson subsampling scheme offers an effective approach for large-scale GLMs.
  • The method provides theoretical guarantees and practical implementation strategies.
  • Simulation studies and real-world data confirm the method's satisfactory performance.