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Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
Efficient Reinforcement Learning High Dimensional Linear Quadratic Systems
2013/4/28
We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control ...
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/11/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...
Grouping Strategies and Thresholding for High Dimensional Linear Models
Structured sparsity Grouping, Learning Theory Non Linear Methods Block-thresholding coherence Wavelets
2012/9/19
The estimation problem in a high regression model with structured sparsity is investigated.An algorithm using a two steps block thresholding procedure called GR-LOL is provided.Convergence rates are p...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
high-dimensional linear model out-of-sample estimators
2011/3/21
We study the unconditional out-of-sample prediction error (predictive risk) associated with two classes of smooth shrinkage estimators for the linear model: James-Stein type shrinkage estimators and r...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
Shrinkage estimators for out-of-sample high-dimensional linear models
2011/3/23
We study the unconditional out-of-sample prediction error (predictive risk) associated with two classes of smooth shrinkage estimators for the linear model: James-Stein type shrinkage estimators and r...
Estimation for High-Dimensional Linear Mixed-Effects Models Using l1-Penalization
Estimation High-Dimensional Linear Mixed-Effects Models l1-Penalization
2010/3/10
We propose an `1-penalized estimation procedure for high-dimensional lin-
ear mixed-effects models. The models are useful whenever there is a grouping
structure among high-dimensional observations, ...
Post-L1-Penalized Estimators in High-Dimensional Linear Regression Models
Post-L1-Penalized Estimators High-Dimensional Linear Regression Models
2010/3/9
In this paper we study the post-penalized estimator which applies ordinary,
unpenalized linear regression to the model selected by the first step penalized estimators,
typically the LASSO. It is wel...
The sparsity and bias of the Lasso selection in high-dimensional linear regression
Penalized regression high-dimensional data variable selection bias rate consistency spectral analysis random matrices
2010/4/30
Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436–1462]
showed that, for neighborhood selection in Gaussian graphical models,
under a neighborhood stability condition, the LASSO is consistent,
...