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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.2K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.0K
Binomial Probability Distribution01:15

Binomial Probability Distribution

16.0K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.0K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

3.7K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
3.7K
Probability Distributions01:32

Probability Distributions

12.2K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
12.2K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.8K
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:
6.8K

You might also read

Related Articles

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

Sort by
Same author

Joint modeling of multiple longitudinal biomarkers and survival outcomes via threshold regression: variability as a predictor.

Biometrics·2026
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

IL1β/IL1R1/IRAK4 Drives Inflammatory Ovarian Cancer Seeding at the inflamed sites and Is Reversed by an IRAK4 inhibitor UR241-2.

bioRxiv : the preprint server for biology·2026
Same author

Complement-mediated ADCP as a distinct and finite cytotoxic mechanism of monoclonal antibodies.

Frontiers in immunology·2026
Same author

The Impact of Layering Tobacco 21 Laws and Smoke-Free Laws on U.S. Adolescent Smoking Behaviors.

American journal of preventive medicine·2026
Same author

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

The annals of applied statistics·2026
Same journal

Fully Synthetic Data for Complex Surveys.

Survey methodology·2025
Same journal

A note on multiply robust predictive mean matching imputation with complex survey data.

Survey methodology·2023
Same journal

The anchoring method: Estimation of interviewer effects in the absence of interpenetrated sample assignment.

Survey methodology·2023
Same journal

Optimum allocation for a dual-frame telephone survey.

Survey methodology·2018
Same journal

Combining information from multiple complex surveys.

Survey methodology·2017
Same journal

A nonparametric method to generate synthetic populations to adjust for complex sampling design features.

Survey methodology·2017
See all related articles

Related Experiment Video

Updated: Feb 17, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Bayesian inference for finite population quantiles from unequal probability samples.

Qixuan Chen1, Michael R Elliott2, Roderick J A Little2

  • 1Department of Biostatistics, Columbia University Mailman School of Public Health, 722 West 168 Street, New York, NY 10032.

Survey Methodology
|December 5, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces two Bayesian spline methods for estimating finite population quantiles from unequal probability samples. These novel methods offer improved accuracy and robustness compared to existing techniques, especially for smaller sample sizes.

Keywords:
Bayesian analysisCumulative distribution functionHeteroscedastic errorsPenalized spline regressionSurvey samples

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

8.0K

Related Experiment Videos

Last Updated: Feb 17, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

8.0K

Area of Science:

  • Statistics
  • Survey Methodology
  • Bayesian Inference

Background:

  • Estimating finite population quantiles from survey data with unequal probability sampling presents statistical challenges.
  • Existing methods like sample-weighted, ratio, and difference estimators have limitations in accuracy and robustness.

Purpose of the Study:

  • To develop and evaluate novel Bayesian methods for robust and efficient inference of finite population quantiles.
  • To compare the performance of new methods against established estimators using simulation studies.

Main Methods:

  • Two Bayesian methods using penalized spline regression models were developed.
  • The first method estimates cumulative distribution functions, while the second predicts non-sampled values using spline-based mean and variance functions.

Main Results:

  • Both Bayesian spline methods demonstrated smaller root mean squared errors than traditional estimators.
  • The new methods showed increased robustness to model misspecification compared to regression through the origin models.
  • For small sample sizes, credible intervals from the new methods achieved closer to nominal confidence coverage.

Conclusions:

  • The proposed Bayesian spline-based estimators provide a favorable balance of robustness and efficiency for quantile estimation in unequal probability sampling.
  • These methods offer a valuable alternative for survey data analysis, particularly when dealing with smaller sample sizes or potential model misspecification.