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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
333
Binomial Probability Distribution01:15

Binomial Probability Distribution

13.1K
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,...
13.1K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.5K
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...
4.5K
Poisson Probability Distribution01:09

Poisson Probability Distribution

10.0K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
10.0K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

726
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,...
726
Data: Types and Distribution01:19

Data: Types and Distribution

2.2K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
2.2K

You might also read

Related Articles

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

Sort by
Same author

Peroxisome Proliferator-Activated Receptor (PPARγ) Plays a Protective Role in Cigarette Smoking-Induced Inflammation via AMP-Activated Protein Kinase (AMPK) Signaling.

Medical science monitor : international medical journal of experimental and clinical research·2018
Same author

Spatially Adaptive Varying Correlation Analysis for Multimodal Neuroimaging Data.

IEEE transactions on medical imaging·2018
Same author

Superconductivity in FeSe: The Role of Nematic Order.

Physical review letters·2018
Same author

Suppressed expression of Cbl-b by NF-κB mediates icotinib resistance in EGFR-mutant non-small-cell lung cancer.

Cell biology international·2018
Same author

The independence of and associations among apoptosis, autophagy, and necrosis.

Signal transduction and targeted therapy·2018
Same author

Tyrosine kinase inhibitor-induced IL-6/STAT3 activation decreases sensitivity of EGFR-mutant non-small cell lung cancer to icotinib.

Cell biology international·2018
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Apr 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

15.2K

A Bayesian nonparametric model for spatially distributed multivariate binary data with application to a

Jian Kang1, Nanhua Zhang, Ran Shi

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, U.S.A.

Biometrics
|July 1, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian method for analyzing complex spatial binary data. It effectively models both within-location outcome correlations and between-location spatial correlations for better insights.

Keywords:
Bayesian methodsDrug resistanceGaussian processesSpatially distributed multivariate binary data

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
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

2.1K

Related Experiment Videos

Last Updated: Apr 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

15.2K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
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

2.1K

Area of Science:

  • Statistics
  • Spatial Analysis
  • Biostatistics

Background:

  • Analysis of spatially distributed multivariate binary data is crucial in various research fields.
  • Existing regression models often fail to adequately address both outcome-specific spatial correlations and multivariate outcome correlations at a single location.
  • This limitation hinders accurate modeling of complex spatial binary datasets.

Purpose of the Study:

  • To develop a novel Bayesian nonparametric approach for jointly modeling multivariate spatial binary data.
  • To integrate and appropriately model both types of correlations: within-location multivariate outcome correlations and between-location spatial correlations.
  • To provide a robust statistical framework for analyzing complex spatial binary data.

Main Methods:

  • Employed a multivariate probit model to link binary outcomes to latent Gaussian variables.
  • Utilized Gaussian processes to define spatially correlated random effects, capturing spatial dependencies.
  • Developed an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior computation and model fitting.

Main Results:

  • The proposed Bayesian nonparametric model successfully integrates both multivariate outcome correlations and spatial correlations.
  • Simulation studies demonstrated the model's effectiveness in handling complex spatial binary data structures.
  • Application to a multidrug-resistant tuberculosis case study highlighted the model's practical utility and performance.

Conclusions:

  • The developed Bayesian nonparametric approach offers a powerful tool for the joint modeling of multivariate spatial binary data.
  • This method overcomes limitations of traditional models by simultaneously accounting for both types of correlations.
  • The approach is validated through simulations and a real-world case study, showing its potential for advancing spatial data analysis.