Jove
Visualize
Contact Us

Related Concept Videos

Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Survival Tree01:19

Survival Tree

85
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
85
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

207
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
207

You might also read

Related Articles

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

Sort by
Same journal

Bias reduction in g-computation for covariate adjustment in randomized clinical trials.

Biometrics·2026
Same journal

FLAME: a model for duration-dependent risk accumulation in episodic temporal exposures.

Biometrics·2026
Same journal

Manifold-constrained Gaussian processes for inference of mixed-effects ordinary differential equations with application to pharmacokinetics.

Biometrics·2026
Same journal

Adaptive Bayesian multivariate spline knot inference with prior specifications on model complexity.

Biometrics·2026
Same journal

Distributionally balanced sampling designs.

Biometrics·2026
Same journal

Unsupervised optimal deep transfer learning for classification under general conditional shift.

Biometrics·2026
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 Experiment Video

Updated: Jul 1, 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

10.2K

Diagnostics for regression models with semicontinuous outcomes.

Lu Yang1

  • 1School of Statistics, University of Minnesota, Minneapolis, MN 55455, United States.

Biometrics
|March 12, 2024
PubMed
Summary

Researchers developed new residuals to assess regression models for semicontinuous data, like healthcare expenditures. This tool helps detect model misspecification, improving data analysis accuracy.

Keywords:
goodness-of-fithealthcare expendituresinsurancetweedie distributiontwo-part modelzero-inflation

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.2K

Related Experiment Videos

Last Updated: Jul 1, 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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

6.2K

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Semicontinuous outcomes (e.g., healthcare expenditures) are common in many fields.
  • Standard regression models (Tobit, Tweedie, two-part) are used but require model adequacy checks.
  • Existing diagnostic tools are inadequate for data with a point mass at zero.

Purpose of the Study:

  • To propose a novel residual diagnostic tool for regression models with semicontinuous outcomes.
  • To address the limitations of standard diagnostic methods for data exhibiting a zero-inflated structure.
  • To provide a reliable method for assessing model fit and identifying misspecification.

Main Methods:

  • Development of a new class of residuals applicable to general regression models for semicontinuous data.
  • Theoretical analysis showing uniform distribution under correct specification and deviation under misspecification.
  • Application of the proposed residuals for both in-sample model validation and out-of-sample predictive distribution evaluation.

Main Results:

  • The proposed residuals exhibit a uniform distribution when the regression model is correctly specified.
  • Deviations from the uniform distribution indicate model misspecification.
  • The methodology proved effective in analyzing health expenditure data from the US Medical Expenditure Panel Survey.

Conclusions:

  • The new residuals offer a valuable tool for validating regression models with semicontinuous outcomes.
  • This method enhances the reliability of analyses in fields with zero-inflated data.
  • The approach is broadly applicable to various regression models and datasets.