Factor models and variable selection in high-dimensional regression analysis
From MaRDI portal
Publication:661163
DOI10.1214/11-AOS905zbMath1231.62131arXiv1202.5151OpenAlexW2002605354MaRDI QIDQ661163
Publication date: 21 February 2012
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1202.5151
Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)
Related Items
An introduction to recent advances in high/infinite dimensional statistics, Feature selection for functional data, Worst possible sub-directions in high-dimensional models, Variable selection in partial linear regression with functional covariate, Bayesian factor-adjusted sparse regression, A partial overview of the theory of statistics with functional data, Integrative Factor Regression and Its Inference for Multimodal Data Analysis, Sparse nonparametric model for the detection of impact points of a functional variable, Posterior consistency of factor dimensionality in high-dimensional sparse factor models, Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization, A novel framework for joint sparse clustering and alignment of functional data, Bayesian MIDAS penalized regressions: estimation, selection, and prediction, Functional linear regression with points of impact, Robust high-dimensional factor models with applications to statistical machine learning, Comments on: ``Probability enhanced effective dimension reduction for classifying sparse functional data, Factor-Adjusted Regularized Model Selection, Variable selection in functional regression models: a review, Sparse nonparametric model for regression with functional covariate, Nonsparse Learning with Latent Variables, On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Functional linear regression analysis for longitudinal data
- The Dantzig selector and sparsity oracle inequalities
- Prediction in functional linear regression
- CLT in functional linear regression models
- Methodology and convergence rates for functional linear regression
- Smoothing splines estimators for functional linear regression
- Functional linear model
- Simultaneous analysis of Lasso and Dantzig selector
- High-dimensional generalized linear models and the lasso
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Regularized estimation of large covariance matrices
- High-dimensional graphs and variable selection with the Lasso
- Inference for Density Families Using Functional Principal Component Analysis
- Forecasting Using Principal Components From a Large Number of Predictors
- Linear functional regression: The case of fixed design and functional response
- Panel Data Models With Interactive Fixed Effects
- Probability Inequalities for Sums of Bounded Random Variables
- On Properties of Functional Principal Components Analysis
- Inferential Theory for Factor Models of Large Dimensions
- Determining the Number of Factors in Approximate Factor Models
- The Generalized Dynamic Factor Model