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

Percentile01:18

Percentile

8.1K
A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
8.1K
Quartile01:15

Quartile

8.2K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
8.2K
Probability Distributions01:32

Probability Distributions

11.3K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.9K
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...
4.9K
5-Number Summary01:04

5-Number Summary

5.4K
In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
5.4K
Chi-square Distribution01:10

Chi-square Distribution

6.1K
How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Factors Associated With Long-Term Visual Field Variability in Patients With Normal-Tension Glaucoma.

Journal of ophthalmology·2026
Same authorSame journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
Same author

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.

Statistics in biosciences·2026
Same author

Target Trial Emulation of Vaccine Effectiveness in 5- to 17-years-olds with Prior SARS-CoV-2 Infection.

Nature communications·2026
Same author

Development of an Early-Phase Local Model for Pandemics Using Public Health Data: Application to the COVID-19 Pandemic.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

Functional generalized estimating equation model to detect glaucomatous visual field progression.

International journal of ophthalmology·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Dec 7, 2025

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

822

Quantile Function on Scalar Regression Analysis for Distributional Data.

Hojin Yang1, Veerabhadran Baladandayuthapani1, Arvind U K Rao2

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.

Journal of the American Statistical Association
|September 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces quantile functional regression to model entire pixel intensity distributions in radiomics, revealing associations between patient data and tumor heterogeneity. The novel approach uncovers key differences in Glioblastoma Multiforme imaging, such as between sexes and genetic mutations.

Keywords:
Basis FunctionsBayesian ModelingFunctional RegressionImaging GeneticsMarkov chain Monte CarloProbability Density Function

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.6K

Related Experiment Videos

Last Updated: Dec 7, 2025

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

822
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.6K

Area of Science:

  • Radiomics and Medical Imaging Analysis
  • Statistical Modeling and Functional Data Analysis
  • Cancer Heterogeneity Research

Background:

  • Radiomics extracts quantitative image features for cancer heterogeneity analysis.
  • Current methods face multiple testing issues and may miss distributional insights.
  • Modeling the entire marginal distribution of pixel intensities offers a more comprehensive approach.

Purpose of the Study:

  • To develop a novel method, quantile functional regression, for modeling the entire marginal distribution of pixel intensities in radiomics.
  • To investigate the effects of demographic, clinical, and genetic predictors on imaging-based cancer heterogeneity.
  • To identify specific distributional features associated with these predictors.

Main Methods:

  • Utilized quantile functions to represent pixel intensity distributions as functional data.
  • Developed custom basis functions called 'quantlets' for modeling quantile functions, ensuring smoothness and statistical power.
  • Employed a Bayesian framework with nonlinear shrinkage and Markov chain Monte Carlo for model fitting and inference.
  • Applied the method to Magnetic Resonance Imaging (MRI) data from Glioblastoma Multiforme.

Main Results:

  • Demonstrated the effectiveness of basis space modeling through simulation studies.
  • Identified significant differences in tumor pixel intensity distributions between males and females in Glioblastoma Multiforme.
  • Found distinct imaging patterns associated with the presence or absence of DDIT3 mutations.

Conclusions:

  • Quantile functional regression provides a powerful framework for analyzing radiomic data by modeling entire intensity distributions.
  • This method offers a global assessment of covariate effects on tumor heterogeneity and identifies specific distributional characteristics.
  • The findings highlight the potential of advanced statistical modeling in uncovering clinically relevant imaging biomarkers for Glioblastoma Multiforme.