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

Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

314
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
314
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

290
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
290
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

318
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
318
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.3K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.3K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

5.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
5.6K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.1K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Tracheal, bronchus, and lung cancer mortality and air pollution exposure in Tuscany, Italy: Bayesian Health Impact Assessment and Global Sensitivity Analysis on a sub-regional scale.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios.

BMC medical research methodology·2024
Same author

[School-based screening strategies to prevent the spread of COVID-19 in school: a systematic review of the literature].

Epidemiologia e prevenzione·2023
Same author

Case-Control Study on the Routes of Transmission of SARS-CoV-2 after the Third Pandemic Wave in Tuscany, Central Italy.

International journal of environmental research and public health·2023
Same author

Schrödinger's Cat Meets Occam's Razor.

Entropy (Basel, Switzerland)·2022
Same author

Combining and comparing regional SARS-CoV-2 epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and global sensitivity analysis.

Frontiers in public health·2022
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

A Nonparametric Bayesian Approach to the Rare Type Match Problem.

Giulia Cereda1, Richard D Gill1

  • 1Mathematical Institute, Leiden University, Postbus 9512, 2300 RA Leiden, The Netherlands.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

The rare type match problem in criminal cases involves DNA profiles not found in databases. This study introduces a Bayesian method to calculate likelihood ratios, simplifying DNA evidence evaluation for forensic science.

Keywords:
Bayesian nonparametricY-STRforensic statisticslikelihood ratiorare type match problem

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.5K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Related Experiment Videos

Last Updated: Nov 27, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.5K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Area of Science:

  • Forensic Science
  • Statistical Genetics
  • Computational Biology

Background:

  • The rare type match problem arises when a suspect's DNA profile matches a crime stain but is absent from reference databases.
  • Evaluating such matches requires calculating likelihood ratios based on unknown population proportions of DNA profiles.
  • Current methods face challenges due to the scarcity of data on rare DNA profiles.

Purpose of the Study:

  • To propose a novel Bayesian nonparametric method for addressing the rare type match problem in forensic DNA analysis.
  • To develop a statistically robust approach for calculating likelihood ratios when suspect profiles are not in reference databases.
  • To simplify the computation of likelihood ratios using an Empirical Bayes approach.

Main Methods:

  • A Bayesian nonparametric model utilizing a two-parameter Poisson Dirichlet distribution as a prior for ranked population proportions.
  • Discarding specific DNA profile names to focus on their relative frequencies.
  • Validation using European Y-STR (Y-chromosome Short Tandem Repeat) DNA profile data.

Main Results:

  • The proposed method effectively handles the rare type match problem by modeling population proportions.
  • The Empirical Bayes approach significantly simplifies the calculation of likelihood ratios.
  • The model provides a robust framework for DNA profile comparison in forensic investigations.

Conclusions:

  • The developed Bayesian nonparametric method offers a reliable solution for the rare type match problem.
  • This approach enhances the accuracy and efficiency of DNA evidence interpretation in criminal justice.
  • The simplification of likelihood ratio calculation has practical implications for forensic laboratories.