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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

395
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
395
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

177
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...
177
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

66
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
66
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

203
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
203
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

449
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...
449
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
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...
4.1K

You might also read

Related Articles

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

Sort by
Same author

Impact of Harvest Timing and Stir-Frying on the Bioactive Compounds, Bioactivities, and Flavor of <i>Ziziphi Spinosae Semen</i>: An Integrated Analysis via GC-IMS, Electronic Sensors, and <i>Caenorhabditis elegans</i> Model.

Plants (Basel, Switzerland)·2026
Same author

Natural versus GnRHa-HRT cycle for FET in tubal infertility with prior failed natural cycles: a propensity score-matched retrospective cohort study.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same author

Natural variations in GhNF-YB3 contribute to seed cotton yield by modulating source-to-sink sucrose allocation.

The Plant cell·2026
Same author

Calumenin prevents fibroblast senescence and lung aging by promoting vimentin proteostasis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.

iScience·2025
Same author

Macrophages and Tissue Homeostasis: From Physiological Functions to Disease Onset.

Frontiers in bioscience (Landmark edition)·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: Jun 19, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

Semiparametric function-on-function quantile regression model with dynamic single-index interactions.

Hanbing Zhu1, Yuanyuan Zhang2, Yehua Li3

  • 1School of Big Data and Statistics, Anhui University, Hefei 230601, China.

Computational Statistics & Data Analysis
|July 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible semiparametric model for analyzing longitudinal data, capturing complex time-dynamic interactions. The new quantile regression approach enhances understanding of how multiple factors influence outcomes over time.

Keywords:
B-splineCheck loss minimizationFunctional dataScore testSemiparametric quantile regressionSingle-index interaction

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K

Related Experiment Videos

Last Updated: Jun 19, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Longitudinal data analysis requires models that can capture complex interactions.
  • Existing quantile regression models for longitudinal data often lack flexibility in modeling time-dynamic effects.

Purpose of the Study:

  • To propose a novel semiparametric function-on-function quantile regression model.
  • To incorporate time-dynamic single-index interactions for multivariate longitudinal/functional covariates.
  • To provide a flexible framework that encompasses existing models as special cases.

Main Methods:

  • Approximation of bivariate nonparametric coefficient functions using tensor product B-splines.
  • Estimation of coefficient functions and index parameters via check loss minimization.
  • Establishment of asymptotic normality for estimated single-index coefficients and convergence rates for coefficient functions.

Main Results:

  • The proposed model effectively captures nonlinear time-dynamic interaction effects.
  • Asymptotic properties of the estimators are theoretically established.
  • A score test is developed to detect interaction effects.

Conclusions:

  • The new semiparametric model offers a flexible and powerful tool for longitudinal data analysis.
  • The method provides robust estimation and theoretical guarantees for interaction effects.
  • Demonstrated utility through simulations and real-world data analysis.