Bayesian factor-adjusted sparse regression (Q2155305)

scientific article; zbMATH DE number 7557275

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English	Bayesian factor-adjusted sparse regression	scientific article; zbMATH DE number 7557275

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instance of

scholarly article

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title

Bayesian factor-adjusted sparse regression (English)

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Journal of Econometrics

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publication date

15 July 2022

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full work available at URL

https://arxiv.org/abs/1903.09741

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zbMATH Keywords

factor model

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Bayesian sparse regression

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posterior contraction

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model selection

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MaRDI profile type

Publication

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cites work

Eigenvalue ratio test for the number of factors

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Generalized double Pareto shrinkage

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Inferential Theory for Factor Models of Large Dimensions

0 references

Determining the Number of Factors in Approximate Factor Models

0 references

Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions

0 references

A simple proof of the restricted isometry property for random matrices

0 references

Sparse models and methods for optimal instruments with an application to eminent domain

0 references

Dirichlet–Laplace Priors for Optimal Shrinkage

0 references

Simultaneous analysis of Lasso and Dantzig selector

0 references

Statistics for high-dimensional data. Methods, theory and applications.

0 references

Sparsity oracle inequalities for the Lasso

0 references

The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).

0 references

Bayesian linear regression with sparse priors

0 references

The Rotation of Eigenvectors by a Perturbation. III

0 references

Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ <sup>1</sup> minimization

0 references

Stable recovery of sparse overcomplete representations in the presence of noise

0 references

Uncertainty principles and ideal atomic decomposition

0 references

High dimensional covariance matrix estimation using a factor model

0 references

Factor-Adjusted Regularized Model Selection

0 references

Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

0 references

High-dimensional covariance matrix estimation in approximate factor models

0 references

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

0 references

I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error

0 references

Sure Independence Screening for Ultrahigh Dimensional Feature Space

0 references

A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)

0 references

Robust high-dimensional factor models with applications to statistical machine learning

0 references

Convergence rates of posterior distributions.

0 references

On Consistency and Sparsity for Principal Components Analysis in High Dimensions

0 references

Factor models and variable selection in high-dimensional regression analysis

0 references

Factor modeling for high-dimensional time series: inference for the number of factors

0 references

Contour projected dimension reduction

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Bayesian variable selection with shrinking and diffusing priors

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The Bayesian Lasso

0 references

Local Shrinkage Rules, Lévy Processes and Regularized Regression

0 references

The Spike-and-Slab LASSO

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Rates of convergence of posterior distributions.

0 references

Stochastic Perturbation Theory

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Forecasting Using Principal Components From a Large Number of Predictors

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SLOPE is adaptive to unknown sparsity and asymptotically minimax

0 references

Q4864293

0 references

High-Dimensional Probability

0 references

Asymptotics of empirical eigenstructure for high dimensional spiked covariance

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On the computational complexity of high-dimensional Bayesian variable selection

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A useful variant of the Davis–Kahan theorem for statisticians

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The sparsity and bias of the LASSO selection in high-dimensional linear regression

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Q3174050

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JECONOM.2020.06.012

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Sitelinks

Mathematics(1 entry)

mardi Publication:2155305