TRES: An R Package for Tensor Regression and Envelope Algorithms
Dublin Core
Title
TRES: An R Package for Tensor Regression and Envelope Algorithms
Subject
dimension reduction, envelopes, manifold optimization, tensor regression
Description
Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES
provides the standard least squares estimators and the more efficient envelope estimators for the tensor response regression (TRR) and the tensor predictor regression (TPR)
models. Envelope methodology provides a relatively new class of dimension reduction
techniques that jointly models the regression mean and covariance parameters. Three
types of widely applicable envelope estimation algorithms are implemented and applied
to both TRR and TPR models.
provides the standard least squares estimators and the more efficient envelope estimators for the tensor response regression (TRR) and the tensor predictor regression (TPR)
models. Envelope methodology provides a relatively new class of dimension reduction
techniques that jointly models the regression mean and covariance parameters. Three
types of widely applicable envelope estimation algorithms are implemented and applied
to both TRR and TPR models.
Creator
Jing Zeng
Source
https://www.jstatsoft.org/article/view/v099i12
Publisher
Florida State University
Date
August 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Jing Zeng, “TRES: An R Package for Tensor Regression and Envelope Algorithms,” Repository Horizon University Indonesia, accessed March 13, 2025, https://repository.horizon.ac.id/items/show/8213.