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

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.2K
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...
9.2K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.5K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.5K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.8K
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...
5.8K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

10.0K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
10.0K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.3K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.3K
Life Tables01:22

Life Tables

670
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
670

You might also read

Related Articles

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

Sort by
Same author

Cause-of-death data series to monitor cause-specific mortality across time and space.

Scientific data·2026
Same author

A Bayesian Framework to Account for Misclassification Error and Uncertainty in the Estimation of Abortion Prevalence.

Studies in family planning·2026
Same author

Small Area Estimation of Education Levels in Low- and Middle-Income Countries.

The annals of applied statistics·2026
Same author

The Limits of Predicting Individual-Level Longevity: Insights From the U.S. Health and Retirement Study.

Demography·2026
Same author

Multi-Morbidity at Death and the US Disadvantage in Mortality.

European journal of population = Revue europeenne de demographie·2025
Same author

Discussion of "Data fission: splitting a single data point".

Journal of the American Statistical Association·2025

Related Experiment Video

Updated: Apr 18, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K

Jointly Estimating Subnational Mortality for Multiple Populations.

Ameer Dharamshi1, Monica Alexander2, Celeste Winant3

  • 1Department of Biostatistics, University of Washington, Seattle, USA.

Demographic Research
|April 17, 2026
PubMed
Summary

Estimating subnational mortality rates is crucial for health policy. Our new Bayesian model accurately estimates age-specific mortality rates, even with small populations and few deaths, revealing geographic variations.

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

15.5K
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

2.8K

Related Experiment Videos

Last Updated: Apr 18, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
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

15.5K
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

2.8K

Area of Science:

  • Demography
  • Biostatistics
  • Public Health

Background:

  • Accurate subnational mortality rate estimation is vital for local health policy.
  • Small populations and sparse death counts present significant challenges in subnational analysis.
  • These challenges are amplified when examining mortality differences across socioeconomic or demographic subgroups.

Purpose of the Study:

  • To develop a statistical model for estimating subnational age-specific mortality rates.
  • The model aims to account for shared mortality experiences and dependencies across different subpopulations.
  • Enhancing the precision of mortality estimates in areas with limited data.

Main Methods:

  • A Bayesian hierarchical model utilizing principal components analysis was developed.
  • The model explicitly incorporates correlations in mortality patterns across subpopulations.
  • This approach allows for borrowing strength across related populations to improve estimates.

Main Results:

  • The model demonstrated strong performance in simulation studies and validation exercises.
  • Application to US county-level data provided estimates of age- and sex-specific mortality rates.
  • Results indicated significant temporal and geographic variations in mortality trends across the United States.

Conclusions:

  • The developed Bayesian model effectively addresses challenges in subnational mortality estimation, particularly with small populations.
  • The findings highlight substantial geographic disparities in mortality trends within the US.
  • This methodology offers a valuable tool for informing local public health policies and interventions.