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EigenPrism:Inference for High-Dimensional Signal-to-Noise Ratios
EigenPrism High-Dimensional Signal Noise Ratios
2015/6/17
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional (p > n) regression estimator, (2) the linear...
Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
Union of Subspaces Group Sparsity Convex Optimization Structured Sparsity Compressed Sensing
2012/11/22
In applications ranging from communications to genetics, signals can be modeled as lying in a union of subspaces. Under this model, signal coefficients that lie in certain subspaces are active or inac...
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 ...
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Bayesian Learning Temporally Correlated Signal Recovery
2011/3/23
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorith...
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Signal Recovery Temporally Correlated Bayesian Learning
2011/3/22
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorith...
Manifold-Based Signal Recovery and Parameter Estimation from Compressive Measurements
Manifolds dimensionality reduction random projections Compressive Sensing spar-sity signal recovery parameter estimation
2010/3/10
A field known as Compressive Sensing (CS) has recently emerged to help address the growing
challenges of capturing and processing high-dimensional signals and data sets. CS exploits the
surprising f...
Minimax Signal Detection in Ill-Posed Inverse Problems
Minimax Signal Detection Ill-Posed Inverse Problems
2010/3/9
We consider the signal detection problem for mildly, severely and extremely
ill-posed inverse problems with Sobolev, analytic and generalized analytic classes
of functions under the Gaussian white n...
Perturbation expansions of signal subspaces for long signals
Perturbation expansions signal subspaces long signals
2010/3/9
Singular Spectrum Analysis and many other subspace-based methods
of signal processing are implicitly relying on the assumption of close prox-
imity of unperturbed and perturbed signal subspaces extr...