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

Stratified Sampling Method01:16

Stratified Sampling Method

12.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
12.8K
Multiple Regression01:25

Multiple Regression

3.2K
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...
3.2K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

153
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
153
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

293
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...
293
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

277
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
277
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100

You might also read

Related Articles

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

Sort by
Same author

Distinct Metabolomic and Lipidomic Profiles Across Donation after Circulatory Death Recovery Strategies Reveal a Common Signature Associated with Primary Graft Dysfunction.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation·2026
Same author

Immune-Evasive Biomimetic Gold-Carbon Nanoplatform for Dual-Modal Theranostics of Hepatocellular Carcinoma.

Small methods·2026
Same author

Probing picometre-scale interlayer deformations via hyperbolic polaritons.

Nature·2026
Same author

Self-Oxygenating Biomimetic Nanoplatform for Synergistic Photodynamic and Photothermal Therapy of Triple-Negative Breast Cancer.

ACS applied materials & interfaces·2026
Same author

Utilizing artificial intelligence to optimize psychological trauma intervention in social assistances.

Frontiers in psychology·2026
Same author

Tumor cell membrane modified clinical lCG nanoprobes for NIR fluorescence imaging of targeted triple negative breast cancer.

Biomedical materials (Bristol, England)·2026
Same journal

A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures (with Discussion).

Bayesian analysis·2026
Same journal

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.

Bayesian analysis·2026
Same journal

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.

Bayesian analysis·2026
Same journal

A Two-Component <i>G</i>-Prior for Variable Selection.

Bayesian analysis·2026
Same journal

Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals.

Bayesian analysis·2026
Same journal

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.

Bayesian analysis·2025
See all related articles

Related Experiment Video

Updated: Sep 7, 2025

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

3.4K

Improving multilevel regression and poststratification with structured priors.

Yuxiang Gao1, Lauren Kennedy2, Daniel Simpson1

  • 1Department of Statistical Sciences, University of Toronto, Canada.

Bayesian Analysis
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

Structured prior distributions reduce bias and variance in Multilevel Regression and Poststratification (MRP) survey estimates. This new framework improves accuracy for non-representative US survey data, enhancing statistical reliability.

Keywords:
INLAMultilevel regression and poststratificationStanbias reductionnon-representative datasmall-area estimationstructured prior distributions

More Related Videos

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

14.6K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Related Experiment Videos

Last Updated: Sep 7, 2025

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

3.4K
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

14.6K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Area of Science:

  • Survey Statistics
  • Statistical Modeling
  • Computational Statistics

Background:

  • Estimating population quantities from non-representative samples is a key challenge in survey statistics.
  • Multilevel Regression and Poststratification (MRP) is a model-based method increasingly used for survey estimates.
  • MRP estimates can be biased if underlying population structures are not captured by the model.

Purpose of the Study:

  • To introduce a novel framework for specifying structured prior distributions.
  • To reduce bias in Multilevel Regression and Poststratification (MRP) estimates.
  • To enhance the accuracy of survey estimates derived from non-representative data.

Main Methods:

  • Developed a new framework for structured prior distributions.
  • Employed simulation studies to evaluate the effectiveness of the proposed priors.
  • Applied the methodology to non-representative US survey data.

Main Results:

  • Structured prior distributions significantly reduce bias in MRP estimates.
  • The proposed priors also lead to variance reduction in posterior MRP estimates.
  • Demonstrated efficacy across a diverse range of data scenarios.

Conclusions:

  • Structured prior distributions offer a robust method for bias and variance reduction in MRP.
  • This framework enhances the reliability of survey estimates from non-representative samples.
  • The approach is effective for improving statistical accuracy in practical applications.