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

Confirmation Biases01:31

Confirmation Biases

8.3K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
8.3K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

6.9K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
6.9K
Hindsight Biases01:12

Hindsight Biases

4.3K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.3K
Bias01:22

Bias

7.4K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.4K
Conservation of Small Populations02:04

Conservation of Small Populations

17.5K
Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
17.5K
What is Population Genetics?01:25

What is Population Genetics?

65.0K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
65.0K

You might also read

Related Articles

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

Sort by
Same author

Cancer disparities: Projection, COVID-19, and scenario-based diagnosis delay impact.

PloS one·2025
Same author

Facing up to uncertain life expectancy: the longevity fan charts.

Demography·2010
See all related articles

Related Experiment Video

Updated: Feb 15, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.2K

Small population bias and sampling effects in stochastic mortality modelling.

Liang Chen1, Andrew J G Cairns1, Torsten Kleinow1

  • 1Actuarial Research Centre, Department of Actuarial Mathematics and Statistics and the Maxwell Institute for Mathematical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS Edinburgh, UK.

European Actuarial Journal
|January 12, 2018
PubMed
Summary

Parametric bootstrap methods assess maximum likelihood estimator (MLE) distributions in stochastic mortality models. Population size impacts MLE distribution and dependencies between age, period, and cohort effects.

Keywords:
Age effectBootstrapCohort effectLikelihood ratio testParameter uncertaintyPeriod effectPower of testSmall populationSystematic parameter difference

More Related Videos

Author Spotlight: Innovative Laser Techniques for Hoechst Staining to Analyze Side Population Cells
06:31

Author Spotlight: Innovative Laser Techniques for Hoechst Staining to Analyze Side Population Cells

Published on: August 23, 2024

2.3K
An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression
06:27

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression

Published on: July 11, 2025

1.0K

Related Experiment Videos

Last Updated: Feb 15, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.2K
Author Spotlight: Innovative Laser Techniques for Hoechst Staining to Analyze Side Population Cells
06:31

Author Spotlight: Innovative Laser Techniques for Hoechst Staining to Analyze Side Population Cells

Published on: August 23, 2024

2.3K
An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression
06:27

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression

Published on: July 11, 2025

1.0K

Area of Science:

  • Actuarial Science
  • Biostatistics
  • Demography

Background:

  • Stochastic mortality models are crucial for long-term financial projections.
  • Understanding the behavior of estimators in finite samples is vital for reliable predictions.
  • The interplay between age, period, and cohort effects requires careful statistical examination.

Purpose of the Study:

  • To investigate the finite sample distribution of the maximum likelihood estimator (MLE) in stochastic mortality models.
  • To analyze the influence of population size on the MLE's distribution and dependencies.
  • To evaluate the performance of a likelihood ratio test (LRT) for parameter hypotheses.

Main Methods:

  • Parametric bootstrap methods were employed to simulate data and assess estimator distributions.
  • The study focused on the dependency structure of MLEs for age, period, and cohort effects.
  • A likelihood ratio test (LRT) was developed and analyzed for hypothesis testing.

Main Results:

  • The finite sample distribution of the MLE is significantly influenced by population size.
  • Dependencies between age, period, and cohort effect estimators were quantified.
  • The LRT was applied to real-world mortality data, providing insights into cohort effects.

Conclusions:

  • Parametric bootstrapping offers a robust approach to understanding MLE behavior in mortality modeling.
  • Population size is a critical factor affecting the precision and reliability of mortality parameter estimates.
  • The LRT provides a valuable tool for hypothesis testing in cohort effect analysis, with demonstrated application to UK mortality data.