Search research articles

Related Concept Videos

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

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...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

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...

Binomial Probability Distribution

Binomial Probability Distribution

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,...

Choosing Between z and t Distribution

Choosing Between z and t Distribution

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...

Probability Distributions

Probability Distributions

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...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

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:

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

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Feb 17, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

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

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 analysis Cumulative distribution function Heteroscedastic errors Penalized spline regression Survey samples

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Related Experiment Videos

Last Updated: Feb 17, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

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.