Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Synthesis and Decomposition Reactions

Synthesis and Decomposition Reactions

Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes.

Inertia Tensor

Inertia Tensor

The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...

Pharmacodynamic Models: Linear Concentration–Effect Model

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...

The Mantel-Cox Log-Rank Test

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...

Linear Equations

Linear Equations

Linear equations form the foundation of many algebraic and real-world applications, characterized by their simplicity and utility. A linear equation is an algebraic statement in which each term is either a constant or a product of a constant and a single variable. These equations represent straight lines when plotted on a Cartesian coordinate plane, reflecting a constant rate of change between two quantities.A typical linear equation in one variable has the form: ax + b = c, where a, b, and c...

Linear Circuits

Linear Circuits

A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...

You might also read

Related Articles

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

Sort by

Same author

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026

Same author

Scalable and robust regression models for continuous proportional data.

Journal of the American Statistical Association·2026

Same author

Local graph estimation with pathwise false discovery control.

Nature communications·2026

Same author

Bayesian Transfer Learning.

Statistical science : a review journal of the Institute of Mathematical Statistics·2026

Same author

Domain Adaptive Bootstrap Aggregating.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2026

Same author

Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals.

Bayesian analysis·2026

Same journal

Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data.

Annals of statistics·2026

Same journal

One-Step Estimation of Differentiable Hilbert-Valued Parameters.

Annals of statistics·2026

Same journal

GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING.

Annals of statistics·2026

Same journal

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026

Same journal

TESTING HIGH-DIMENSIONAL REGRESSION COEFFICIENTS IN LINEAR MODELS.

Annals of statistics·2026

Same journal

COUNTERFACTUAL INFERENCE IN SEQUENTIAL EXPERIMENTS.

Annals of statistics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 15, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS.

James E Johndrow¹, Anirban Bhattacharya², David B Dunson¹

¹Duke University.

Annals of Statistics

|January 16, 2018

Summary

This summary is machine-generated.

This study connects log-linear models and latent structure analysis for categorical data. A new tensor decomposition method is introduced, offering a more flexible way to analyze complex data structures.

Keywords:

Bayesian Parafac Tucker categorical data contingency table graphical model high-dimensional latent class analysis low rank sparsity

More Related Videos

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

Related Experiment Videos

Last Updated: Feb 15, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

Area of Science:

Statistics
Data Analysis
Multivariate Categorical Data

Background:

Contingency table analysis commonly uses log-linear models.
Latent structure analysis offers an alternative approach.
The relationship between dimensionality reduction in these paradigms is not well understood.

Purpose of the Study:

To explore the connection between log-linear models and latent structure analysis.
To introduce a novel tensor decomposition framework for multivariate categorical data.
To provide a more flexible and parsimonious characterization of data.

Main Methods:

Deriving results relating log-linear model support to nonnegative tensor ranks.
Proposing a new collapsed Tucker class of tensor decompositions.
Employing a Bayesian approach for statistical inference.

Main Results:

Established theoretical links between log-linear model sparsity and tensor rank.
Introduced a new class of tensor decompositions bridging PARAFAC and Tucker methods.
Demonstrated empirical advantages of the proposed decompositions.

Conclusions:

The new tensor decompositions offer a unified and flexible framework.
This work enhances the analysis of multivariate categorical data.
Bayesian inference proved effective for the proposed models.