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

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

217
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...
217
Censoring Survival Data01:09

Censoring Survival Data

109
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...
109
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
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...
1.6K
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

540
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...
540
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

451
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...
451

You might also read

Related Articles

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

Sort by
Same author

IDENTIFICATION AND INFERENCE FOR MARGINAL AVERAGE TREATMENT EFFECT ON THE TREATED WITH AN INSTRUMENTAL VARIABLE.

Statistica Sinica·2020
Same author

A Logic-Memory Transistor with the Integration of Visible Information Sensing-Memory-Processing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2020
Same author

Antitumor Activity of a Novel Double-Targeted System for Folate Receptor-Mediated Delivery of Mitomycin C.

ACS omega·2020
Same author

Identification of NEO1 as a prognostic biomarker and its effects on the progression of colorectal cancer.

Cancer cell international·2020
Same author

SiRNA targeting PFK1 inhibits proliferation and migration and enhances radiosensitivity by suppressing glycolysis in colorectal cancer.

American journal of translational research·2020
Same author

Corrigendum to "Dexmedetomidine protects H9C2 against hypoxia/reoxygenation injury through miR-208b-3p/Med13/Wnt signaling pathway axis" [Biomed. Pharmacother. 125 (2020) 110001].

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2020
Same journal

Variable selection for single-index varying-coefficients models with applications to synergistic G × E interactions.

Electronic journal of statistics·2026
Same journal

Selecting massive variables using an iterated conditional modes/medians algorithm.

Electronic journal of statistics·2026
Same journal

Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.

Electronic journal of statistics·2026
Same journal

Selective Inference for Sparse Graphs via Neighborhood Selection.

Electronic journal of statistics·2025
Same journal

Reproducible parameter inference using bagged posteriors.

Electronic journal of statistics·2025
Same journal

Regression analysis of semiparametric Cox-Aalen transformation models with partly interval-censored data.

Electronic journal of statistics·2025
See all related articles

Related Experiment Video

Updated: Jul 13, 2025

Automated Detection and Analysis of Exocytosis
13:28

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

3.5K

Envelope method with ignorable missing data.

Linquan Ma1,2, Lan Liu2, Wei Yang3

  • 1Department of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, USA.

Electronic Journal of Statistics
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced envelope method for multivariate regression, effectively handling missing data. The new approach improves efficiency and reduces bias compared to standard methods, even outperforming complete data analysis.

Keywords:
EM-algorithmEfficiency gainMissing dataMultivariate regressionSufficient dimension reduction

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.6K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Related Experiment Videos

Last Updated: Jul 13, 2025

Automated Detection and Analysis of Exocytosis
13:28

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

3.5K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.6K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • The envelope method reduces dimensionality in multivariate regression.
  • Missing data in complete case analysis can cause bias and inefficiency.
  • Existing methods struggle with missing data in envelope estimation.

Purpose of the Study:

  • To generalize the envelope estimation for multivariate regressions with missing predictors and/or responses.
  • To develop a robust method that addresses the limitations of complete case analysis.
  • To improve the efficiency and accuracy of envelope estimation in the presence of missing data.

Main Methods:

  • Incorporation of the envelope structure within the expectation-maximization (EM) algorithm.
  • Development of a special decomposition to address non-pointwise identifiability of envelope parameters.
  • Generalization of the EM algorithm for missing at random (MAR) data in envelope estimation.

Main Results:

  • The proposed method is guaranteed to be more efficient than the standard EM algorithm.
  • The method shows potential to outperform Maximum Likelihood Estimation (MLE) using full data.
  • Asymptotic properties are established for both normal and non-normal data distributions.

Conclusions:

  • The generalized envelope method provides a statistically sound and efficient approach for multivariate regression with missing data.
  • Simulation studies and a real-world application (CRIC study) confirm the efficiency gains.
  • This method offers a valuable tool for researchers dealing with incomplete datasets in various fields.