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

Distribution and Dispersion00:54

Distribution and Dispersion

To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:

You might also read

Related Articles

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

Sort by
Same author

Histological endometrial dating: a reliable tool for personalized frozen-thawed embryo transfer in patients with repeated implantation failure in natural cycles.

BMC pregnancy and childbirth·2023
Same author

A 1-kb human CDCA8 promoter directs the spermatogonia-specific luciferase expression in adult testis.

Gene·2023
Same author

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data.

Journal of proteome research·2023
Same author

C9orf131 and C10orf120 are not essential for male fertility in humans or mice.

Developmental biology·2023
Same author

Telomeres cooperate in zygotic genome activation by affecting <i>DUX4</i>/<i>Dux</i> transcription.

iScience·2023
Same author

Development and evaluation of a live birth prediction model for evaluating human blastocysts from a retrospective study.

eLife·2023
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spatial scan statistics with overdispersion.

Tonglin Zhang1, Zuoyi Zhang, Ge Lin

  • 1Department of Statistics, Purdue University, 250 North University Street, West Lafayette, IN 47907-2066, USA. tlzhang@purdue.edu

Statistics in Medicine
|November 5, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a quasi-Poisson spatial scan test to address overdispersion in disease surveillance data. The new method reduces false alarms, improving the reliability of spatial cluster detection.

More Related Videos

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Related Experiment Videos

Last Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Published on: November 27, 2019

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Spatial scan statistics are crucial for disease surveillance and cluster detection.
  • Overdispersion in real-world data violates the Poisson assumption, leading to increased false alarms (Type I errors).

Purpose of the Study:

  • To extend the Poisson-based spatial scan test to a quasi-Poisson-based test to account for overdispersion.
  • To evaluate the performance of the quasi-Poisson test in reducing Type I errors and its application in real-world disease surveillance.

Main Methods:

  • Development of a quasi-Poisson-based spatial scan test.
  • Simulation studies to assess Type I error probabilities under varying degrees of overdispersion.
  • Application of both Poisson and quasi-Poisson tests to a case study of infant mortality in Jiangxi, China.

Main Results:

  • The quasi-Poisson test significantly reduces Type I error probabilities when overdispersion is present.
  • In the infant mortality case study, both tests identified a primary cluster.
  • The Poisson-based test identified a secondary cluster that was not confirmed by the quasi-Poisson test.

Conclusions:

  • The quasi-Poisson spatial scan test is a more reliable method for disease surveillance and cluster detection in the presence of overdispersion.
  • Results from the Poisson-based test should be interpreted cautiously, especially for secondary clusters, if not supported by the quasi-Poisson test.