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

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
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
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...
12.0K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
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

515
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...
515
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

199
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...
199
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Predictors of Sustainability in the Collaborative Care Medicaid Program for Depression: A Cross-Sectional Study.

Psychiatric services (Washington, D.C.)·2026
Same author

BIVARIATE HIERARCHICAL BAYESIAN MODEL FOR COMBINING SUMMARY MEASURES AND THEIR UNCERTAINTIES FROM MULTIPLE SOURCES.

The annals of applied statistics·2026
Same author

A BAYESIAN GROWTH MIXTURE MODEL FOR COMPLEX SURVEY DATA: CLUSTERING POSTDISASTER PTSD TRAJECTORIES.

The annals of applied statistics·2026
Same author

Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026
Same author

Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets.

PLoS computational biology·2025
Same author

Assessment of a gridded population sample frame for a household survey of refugee populations in Uganda, 2021.

International journal of health geographics·2025

Related Experiment Video

Updated: Jul 7, 2025

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

Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A

Swen Kuh1,2, Lauren Kennedy1,2, Qixuan Chen3

  • 1School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia.

Statistics in Medicine
|December 26, 2023
PubMed
Summary

Leave-one-out cross-validation (LOO) methods like PSIS-LOO may not reliably evaluate multilevel regression and poststratification (MRP) models. These techniques struggle to rank models accurately, especially for small-area estimation, suggesting caution in their application for MRP validation.

Keywords:
LOOMRPmodel validationpopulation estimandsmall-area estimation

More Related Videos

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
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Related Experiment Videos

Last Updated: Jul 7, 2025

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
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
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Area of Science:

  • Statistics
  • Computational Statistics
  • Survey Methodology

Background:

  • Multilevel regression and poststratification (MRP) is increasingly used for population inference.
  • Model validity in MRP is crucial but under-researched, particularly regarding validation techniques.
  • Assessing the performance of different MRP models is essential for reliable population estimates.

Purpose of the Study:

  • To evaluate the utility of leave-one-out cross-validation (LOO) for comparing Bayesian models in MRP.
  • To investigate two approximate LOO calculations: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted version (WTD-PSIS-LOO).
  • To assess how accurately these LOO criteria rank models for both population-level and small-area estimation.

Main Methods:

  • Utilized two simulation designs to test PSIS-LOO and WTD-PSIS-LOO performance.
  • Examined model ranking accuracy for population estimands and small-area estimands.
  • Applied the methods to real-world data from the National Health and Nutrition Examination Survey (NHANES).

Main Results:

  • Neither PSIS-LOO nor WTD-PSIS-LOO consistently recovered the correct model order for population estimands, though they identified the best and worst models.
  • Model performance varied across different small areas, complicating validation for small-area estimation.
  • Model ranking showed slight improvement at smaller-area levels when considering different priors.
  • Real-world NHANES data analysis corroborated simulation findings, indicating caution with PSIS-LOO for MRP.

Conclusions:

  • PSIS-LOO-based model validation may not be fully suitable for evaluating MRP methods due to aggregation effects.
  • The aggregation stage in MRP can obscure individual-level prediction errors, impacting LOO performance.
  • These findings highlight the need for careful application and potential re-evaluation of LOO techniques in the context of MRP validation.