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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

102
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
102
Censoring Survival Data01:09

Censoring Survival Data

65
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
65
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

29
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
29
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

97
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
97
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

115
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,...
115
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

367
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...
367

You might also read

Related Articles

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

Sort by
Same author

Identification of G4-regulated immune-related drug targets for prostate cancer based on G4 screen and machine learning.

Frontiers in immunology·2026
Same author

Predicting abnormal auditory brainstem response outcomes using risk factors.

International journal of pediatric otorhinolaryngology·2026
Same author

Transcription factor ID3 promotes fibroblast differentiation and proliferation in lung fibrosis through augmenting the TGF-β signaling pathway.

Molecular immunology·2026
Same author

A Single-Cell Guided Machine Learning Model Predicts Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Journal of chemical information and modeling·2026
Same author

Energy statistic-based modified information criterion for detecting the change in distribution.

Journal of applied statistics·2026
Same author

Anterior tibial translation in pediatric ACL-deficient knees under functional orthosis loading: a finite element study.

The Knee·2026
Same journal

Comparing Two Categorical Gini Correlations with Applications to Classification Problems.

Statistical papers (Berlin, Germany)·2026
Same journal

Handling skewness and directional tails in model-based clustering.

Statistical papers (Berlin, Germany)·2025
Same journal

Maximum likelihood estimation under the Emax model: existence, geometry and efficiency.

Statistical papers (Berlin, Germany)·2025
Same journal

Local linear smoothing for regression surfaces on the simplex using Dirichlet kernels.

Statistical papers (Berlin, Germany)·2025
Same journal

On some problems of Bayesian region construction with guaranteed coverages.

Statistical papers (Berlin, Germany)·2024
Same journal

Osband's principle for identification functions.

Statistical papers (Berlin, Germany)·2024
See all related articles

Related Experiment Video

Updated: Jun 10, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K

Statistical Inferences for Missing Response Problems Based on Modified Empirical Likelihood.

Sima Sharghi1, Kevin Stoll2, Wei Ning3

  • 1Department of Biostatistics and Computational Biology, University of Rochester, New York, USA.

Statistical Papers (Berlin, Germany)
|October 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces improved empirical likelihood (EL) methods for handling missing data in statistical analysis and causal inference. The new estimators demonstrate superior performance in simulations for estimating mean responses and treatment effects.

Keywords:
65C60Adjusted Empirical LikelihoodCausal InferencesMSC 47N30Missing ResponsePropensity ScoreTransformed Empirical Likelihood

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.1K
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

3.3K

Related Experiment Videos

Last Updated: Jun 10, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.9K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.1K
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

3.3K

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Missing data poses significant challenges in statistical inference.
  • Empirical likelihood (EL) is a powerful, non-parametric method for statistical inference.
  • Existing EL methods have limitations in parameter hypothesis testing and handling missing responses.

Purpose of the Study:

  • To modify and advance the empirical likelihood (EL) approach for statistical inference with missing response data.
  • To develop consistent mean estimators and confidence intervals for data with missing responses.
  • To extend EL methods for estimating average treatment effects in causal inference.

Main Methods:

  • Modified the empirical likelihood (EL) approach to address missing response data.
  • Developed consistent mean estimators and confidence intervals.
  • Extended EL for estimating average treatment effect (ATE) in causal inference settings.
  • Proved the consistency of the proposed ATE estimators.

Main Results:

  • Proposed consistent mean estimators and confidence intervals for missing response data.
  • Developed analogous estimators for average treatment effect (ATE).
  • Demonstrated the utility of the estimators in a real-world causal inference scenario.
  • Simulations showed the proposed estimators outperform historical methods in terms of relative Root Mean Squared Error (RMSE) and coverage probability.

Conclusions:

  • The enhanced empirical likelihood (EL) approach provides effective statistical inference for missing response data.
  • The proposed methods offer improved accuracy and reliability in estimating mean responses and average treatment effects.
  • This work advances the application of EL in challenging statistical and causal inference problems.