Reduced rank regression with possibly non-smooth criterion functions: an empirical likelihood approach
From MaRDI portal
Publication:1658991
DOI10.1016/j.csda.2016.04.012zbMath1466.62067OpenAlexW2391068551MaRDI QIDQ1658991
Fukang Zhu, Heng Lian, San Ying Feng
Publication date: 15 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2016.04.012
Cites Work
- Reduced rank regression via adaptive nuclear norm penalization
- Optimal selection of reduced rank estimators of high-dimensional matrices
- Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
- Empirical likelihood ratio confidence regions
- Extending the scope of empirical likelihood
- Bayesian reduced rank regression in econometrics
- Empirical likelihood and general estimating equations
- Asymptotic distribution of the reduced rank regression estimator under general conditions
- Multivariate quantiles and multiple-output regression quantiles: from \(L_{1}\) optimization to halfspace depth
- Penalized empirical likelihood and growing dimensional general estimating equations
- Penalized high-dimensional empirical likelihood
- On a Geometric Notion of Quantiles for Multivariate Data
- Empirical Likelihood Confidence Regions in a Partially Linear Single-Index Model
- Empirical Likelihood for Non‐Smooth Criterion Functions
- Empirical likelihood ratio confidence intervals for a single functional
- Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
- Empirical Likelihood for a Varying Coefficient Model With Longitudinal Data
- Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators
- Reduced Rank Stochastic Regression with a Sparse Singular value Decomposition
- Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions
This page was built for publication: Reduced rank regression with possibly non-smooth criterion functions: an empirical likelihood approach
