Multivariate varying-coefficient models via tensor decomposition

Cites Work

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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Tensor Decompositions and Applications
Tensor-Train Decomposition
Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
Variable selection in nonparametric additive models
Additive regression and other nonparametric models
A dual algorithm for the solution of nonlinear variational problems via finite element approximation
Simultaneous analysis of Lasso and Dantzig selector
Variable selection using MM algorithms
Variable selection and estimation in high-dimensional varying-coefficient models
Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
The Geometry of Algorithms with Orthogonality Constraints
Semiparametric Regression
Procrustes Problems
Varying-coefficient models and basis function approximations for the analysis of repeated measurements
On a Principal Varying Coefficient Model
Dimensionality Reduction and Variable Selection in Multivariate Varying-Coefficient Models With a Large Number of Covariates
Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
Tensor Regression Using Low-Rank and Sparse Tucker Decompositions
Tensor Regression with Applications in Neuroimaging Data Analysis
Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
Model Selection and Estimation in Regression with Grouped Variables
Varying Coefficient Regression Models: A Review and New Developments

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