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

Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.7K
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).
8.7K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

774
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
774
Expected Value01:15

Expected Value

7.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
7.8K
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
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

438
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
438

You might also read

Related Articles

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

Sort by
Same author

WMRNN: Weighted Modal Regression Neural Networks for Right Censored Data.

Statistics in medicine·2026
Same author

From Crystal Structure to Dissolution Enhancement: Cocrystal Engineering of Resmetirom to Address Its Solubility Challenge.

Pharmaceutical research·2026
Same author

A Statistical Framework for Measuring Reproducibility and Replicability of High-Throughput Experiments From Multiple Sources.

Statistics in medicine·2026
Same author

Study on the CHJ01 antitumor activity and mechanism via targeting sphingosine kinase 1 in A549 cells.

Pharmaceutical science advances·2026
Same author

Direct reciprocity in multi-action repeated games.

Journal of theoretical biology·2025
Same author

METTL3 promotes the progression of non-alcoholic fatty liver disease by mediating m6A methylation of FAS.

Scientific reports·2025
Same journal

Modeling Disease-specific Survival in Observational Studies with Missing Cause of Death: Leveraging Information from Clinical Trial Data.

Computational statistics & data analysis·2026
Same journal

A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models.

Computational statistics & data analysis·2025
Same journal

MIXANDMIX: numerical techniques for the computation of empirical spectral distributions of population mixtures.

Computational statistics & data analysis·2024
Same journal

Locally sparse quantile estimation for a partially functional interaction model.

Computational statistics & data analysis·2024
Same journal

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models.

Computational statistics & data analysis·2024
Same journal

GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction.

Computational statistics & data analysis·2024
See all related articles

Related Experiment Video

Updated: Feb 16, 2026

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.3K

A Continuous Threshold Expectile Model.

Feipeng Zhang1,2, Qunhua Li1

  • 1Department of Statistics, Pennsylvania State University, PA, 16802, USA.

Computational Statistics & Data Analysis
|December 20, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces continuous threshold expectile regression for analyzing data with varying covariate effects. The new method efficiently estimates thresholds and provides a computationally advantageous test for their existence.

Keywords:
Expectile regressionGrid search methodThresholdWeighted CUSUM test

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform
06:31

Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform

Published on: August 4, 2022

3.7K

Related Experiment Videos

Last Updated: Feb 16, 2026

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.3K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform
06:31

Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform

Published on: August 4, 2022

3.7K

Area of Science:

  • Statistics
  • Econometrics

Background:

  • Expectile regression extends conditional mean analysis.
  • Modeling threshold effects in regression is crucial for accurate data interpretation.

Purpose of the Study:

  • Develop a continuous threshold expectile regression model.
  • Provide methods for threshold and coefficient estimation.
  • Propose an efficient test for threshold existence.

Main Methods:

  • Grid search approach for threshold and coefficient estimation.
  • Derivation of asymptotic properties for estimators.
  • Weighted CUSUM type test statistic for threshold detection.

Main Results:

  • Root-n consistency achieved for the threshold estimator.
  • The proposed test is computationally efficient compared to likelihood-ratio tests.
  • Simulation studies demonstrate good performance in various conditions.

Conclusions:

  • The continuous threshold expectile regression offers a robust method for analyzing complex data relationships.
  • The developed test provides an efficient way to detect structural breaks.
  • The method is applicable to real-world datasets and available in the R package cthreshER.