搜索结果: 1-15 共查到“统计学 recovery”相关记录15条 . 查询时间(0.169 秒)
On Pattern Recovery of The Fused Lasso
Confidence interval jackknife empirical likelihood risk measure
2016/1/25
Quantifying risks is of importance in insurance. In this paper, we employ the jackknife empirical likelihood method to construct confidence intervals for some risk measures and related quantities stud...
On Pattern Recovery of The Fused Lasso
Fused Lasso Non-asymptotic Pattern recovery Preconditioning
2016/1/20
We study the property of the Fused Lasso Signal Approximator(FLSA) for estimating a blocky signal sequence with additive noise.We transform the FLSA to an ordinary Lasso problem. By studying the prope...
Guaranteed Sparse Recovery under Linear Transformation
Guaranteed Sparse Recovery Linear Transformation
2013/6/13
We consider the following signal recovery problem: given a measurement matrix $\Phi\in \mathbb{R}^{n\times p}$ and a noisy observation vector $c\in \mathbb{R}^{n}$ constructed from $c = \Phi\theta^* +...
Sparse approximation and recovery by greedy algorithms in Banach spaces
Sparse approximation recovery greedy algorithms Banach spaces
2013/4/28
We study sparse approximation by greedy algorithms. We prove the Lebesgue-type inequalities for the Weak Chebyshev Greedy Algorithm (WCGA), a generalization of the Weak Orthogonal Matching Pursuit to ...
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...
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
Stochastic optimization sparse statistical recovery optimal algorithm high dimensions
2012/9/19
We develop and analyze stochastic optimization algorithms for problems in which the ex-pected loss is strongly convex, and the optimum is (approximately)sparse. Previous approaches are able to exploit...
Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals
Tight Measurement Bounds Exact Recovery Structured Sparse Signals
2011/7/6
Standard compressive sensing results state that to exactly recover an s sparse signal in R^p, one requires O(s\cdotlog p) measurements. While this bound is extremely useful in practice, often real wor...
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
Multiple Measurement Vectors Block Sparsity Time-Varying Sparsity
2011/6/16
A trend in compressed sensing (CS) is to exploit struc-
ture for improved reconstruction performance. In the
basic CS model (i.e. the single measurement vec-
tor model), exploiting the clustering s...
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...
Probabilistic Recovery of Multiple Subspaces in Point Clouds by Geometric lp Minimization
Detection and clustering of subspaces in point clouds hybrid linear modeling lp minimizationas relaxation for l0 minimization
2010/3/10
We assume data independently sampled froma mixture distribution on the unit ball of RD withK+1
components: the first component is a uniform distribution on that ball representing outliers and the oth...
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...
Union support recovery in high-dimensional multivariate regression
Union support recovery high-dimensional multivariate regression
2010/4/30
In the problem of multivariate regression, a K-dimensional response vector is regressed
upon a common set of p covariates, with a matrix B 2 RpK of regression
coecients. We study the behavior of ...
We consider the problem of detecting edges in piecewise
smooth functions from their N-degree spectral content, which is assumed
to be corrupted by noise. There are three scales involved: the
“smoot...
Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting
High-dimensional statistical inference subset selection signal denoising compressivesensing model selection
2010/4/26
The problem of recovering the sparsity pattern of a fixed but unknown vector β ∈ Rp
based on a set of n noisy observations arises in a variety of settings, including subset selection in regression, ...