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

Skewness01:06

Skewness

19.0K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
19.0K
Types of Skewness01:09

Types of Skewness

18.3K
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
18.3K
What is Variation?01:14

What is Variation?

18.5K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.5K
Variation01:19

Variation

8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.0K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

28.2K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
28.2K
Solving Inequalities Graphically01:24

Solving Inequalities Graphically

243
Solving inequalities graphically involves using a visual approach to determine where a mathematical expression meets a specific condition, such as being greater than or less than another value. By examining the position of a graph relative to the x-axis or another graph, it becomes possible to identify the range of x-values that satisfy the inequality. This method provides an intuitive understanding of solution intervals by showing where the inequality holds true.Graphical solutions to...
243

You might also read

Related Articles

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

Sort by
Same author

HPV-Adjusted Feature Screening With FDR Control in Head and Neck Cancer.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.

Journal of nonparametric statistics·2026
Same author

MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models.

Journal of agricultural, biological, and environmental statistics·2026
Same author

Biosensing in Healthcare Applications.

Studies in health technology and informatics·2026
Same author

Prognostic value of FDG-PET SUV changes in cervical cancer following radiation therapy: a retrospective cohort study.

Archives of gynecology and obstetrics·2026
Same author

Cardiovascular Risk Factors Among Younger and Older C-AYA Cancer Survivors Treated with Anthracyclines: A Single-Center Analysis.

Cancers·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
Same journal

Addressing the influence of unmeasured confounding in observational studies with time-to-event outcomes: a semiparametric sensitivity analysis approach.

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

Related Experiment Video

Updated: Feb 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.1K

A graphical model for skewed matrix-variate non-randomly missing data.

Lin Zhang1, Dipankar Bandyopadhyay2

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, A430 Mayo Memorial Building, MMC 303, 420 Delaware Street S.E, Minneapolis, MN, USA.

Biostatistics (Oxford, England)
|October 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical model to accurately analyze periodontal disease (PD) progression, accounting for complex data issues like non-normality and missing teeth. The new method improves disease assessment compared to traditional approaches.

Keywords:
BayesianMCMCMatrix-variate dataNon-random missingnessSkew-tSpatial

More Related Videos

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K
Improving the Success Rate of Protein Crystallization by Random Microseed Matrix Screening
12:24

Improving the Success Rate of Protein Crystallization by Random Microseed Matrix Screening

Published on: August 31, 2013

18.3K

Related Experiment Videos

Last Updated: Feb 3, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.1K
Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K
Improving the Success Rate of Protein Crystallization by Random Microseed Matrix Screening
12:24

Improving the Success Rate of Protein Crystallization by Random Microseed Matrix Screening

Published on: August 31, 2013

18.3K

Area of Science:

  • Statistics
  • Epidemiology
  • Dental Research

Background:

  • Periodontal disease (PD) studies collect biomarkers like clinical attachment level (CAL) and probed pocket depth (PPD).
  • Standard linear mixed models (LMMs) assume normality, which is often violated in PD data.
  • PD data exhibits non-normality, spatial correlations between tooth sites, and informative missingness.

Purpose of the Study:

  • To develop a robust statistical framework for analyzing complex periodontal disease data.
  • To address non-normality, spatial dependencies, and non-random missingness in PD biomarkers.
  • To improve the accuracy of epidemiological assessments of periodontal disease.

Main Methods:

  • A matrix-variate skew-t linear mixed model (LMM) with Markov graphical embedding was employed.
  • Spatial associations between bivariate responses (PPD and CAL) were handled using the graphical model.
  • Non-randomly missing data were imputed using a latent probit regression within a hierarchical Bayesian framework.
  • Markov chain Monte Carlo (MCMC) methods were used for parameter estimation.

Main Results:

  • The proposed model demonstrated a significantly improved fit over alternative models.
  • The framework effectively handled non-normality, spatial correlations, and informative missingness in PD data.
  • Parameter estimation benefited from information sharing across hierarchical levels.

Conclusions:

  • The developed statistical approach offers a more accurate and comprehensive method for analyzing periodontal disease progression.
  • This enhanced modeling strategy can lead to better insights into PD risk factors and disease dynamics.
  • The unified Bayesian framework provides a powerful tool for complex epidemiological data analysis.