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

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Incidence and remission of endometriosis in Germany based on prevalence data from 35 million patients from the statutory health insurance.

BMC women's health·2026
Same author

Workflow for Statistical Analysis of Environmental Mixtures.

Environmental health perspectives·2026
Same author

DEVELOPMENT AND APPLICATION OF BRAIN TISSUE BASED MULTI-OMICS PROFILE SCORES FOR ALZHEIMER'S DISEASE.

Research square·2026
Same author

A digital twin for real-time biodiversity forecasting with citizen science data.

Nature ecology & evolution·2026
Same author

Comparing Methods to Assess Treatment Effect Heterogeneity in General Parametric Regression Models.

Statistics in medicine·2026
Same author

Socio-spatial characterization of sub-sewersheds for wastewater-based epidemiology (WBE): Developing and evaluating two estimators for population-related variables.

Spatial and spatio-temporal epidemiology·2025
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Stochastically ordered multiple regression.

Björn Bornkamp1, Katja Ickstadt, David Dunson

  • 1Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany. bornkamp@statistik.uni-dortmund.de

Biostatistics (Oxford, England)
|February 13, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian nonparametric method for modeling health responses to exposures, improving efficiency by incorporating prior knowledge about effect directions. The approach enhances predictions in epidemiological studies.

Related Experiment Videos

Last Updated: Jun 16, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

  • Biostatistics
  • Epidemiology
  • Computational Statistics

Background:

  • Prior information on exposure-response relationships is common in epidemiology.
  • Stochastic ordering assumptions can improve the efficiency of nonparametric modeling.

Purpose of the Study:

  • To propose a Bayesian nonparametric method for modeling conditional response distributions with directional prior information.
  • To enhance prediction accuracy and model efficiency in epidemiological studies.

Main Methods:

  • Characterizing conditional response density using Gaussian mixture models.
  • Allowing Gaussian mean locations to vary flexibly with predictors under stochastic ordering constraints.
  • Developing Markov chain Monte Carlo methods for posterior computation.

Main Results:

  • Demonstrated theoretical properties of the proposed Bayesian nonparametric approach.
  • Illustrated the method's utility through simulation studies.
  • Applied the method to a reproductive epidemiology case.

Conclusions:

  • The proposed Bayesian nonparametric approach effectively incorporates directional prior information.
  • This method offers improved efficiency and accuracy in modeling complex exposure-response relationships.
  • The approach is valuable for analyzing epidemiological data with known effect directions.