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Inference about ATE from Observational Studies with Continuous Outcome and Unmeasured Confounding
Inference about ATE Observational Studies Continuous Outcome Unmeasured Confounding
2013/4/28
For settings with a binary treatment and a binary outcome, instrumental variables can be used to construct bounds on a causal treatment effect. With continuous outcomes, meaningful bounds are more dif...
Template matching with noisy patches: A contrast-invariant GLR test
Template matching Likelihood ratio test Detection theory Image restoration
2013/4/28
Matching patches from a noisy image to atoms in a dictionary of patches is a key ingredient to many techniques in image processing and computer vision. By representing with a single atom all patches t...
Template matching with noisy patches: A contrast-invariant GLR test
Template matching Likelihood ratio test Detection theory Image restoration
2013/4/28
Matching patches from a noisy image to atoms in a dictionary of patches is a key ingredient to many techniques in image processing and computer vision. By representing with a single atom all patches t...
A Diffusion Process on Riemannian Manifold for Visual Tracking
racking Particle filtering Template update Generative Template Model Riemannian manifolds log-transformed space.
2013/5/2
Robust visual tracking for long video sequences is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updat...
Volatility Inference in the Presence of Both Endogenous Time and Microstructure Noise
It^o Process Realized Volatility Integrated Volatility Time Endogeneity Market Microstructure Noise
2013/5/2
In this article we consider the volatility inference in the presence of both market microstructure noise and endogenous time. Estimators of the integrated volatility in such a setting are proposed, an...
Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relativ...
Sharp Variable Selection of a Sparse Submatrix in a High-Dimensional Noisy Matrix
estimation minimax testing random matrices selection of sparse signal sharp selection bounds variable selection
2013/4/28
We observe a $N\times M$ matrix of independent, identically distributed Gaussian random variables which are centered except for elements of some submatrix of size $n\times m$ where the mean is larger ...
Generalized Measures for the Evaluation of Community Detection Methods
Complex Networks Community Detection Evaluation Measure Cluster Analysis Purity Adjusted Rand Index Normalized Mutual Information
2013/5/2
Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating ...
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Sparse approximation Regularization Fused-LASSO Split Bregman Multidimensional signals
2013/5/2
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization prob...
Adding a systematic uncertainty to the signal estimation in the on/off-zone measurements
Adding a systematic uncertainty the signal estimation in the on/off-zone measurements
2013/4/28
The measurements with the background estimation from an off-zone are widely used in astrophysics, accelerator physics and other areas. Usually, the expected number of the background events in the off-...
Node-Based Learning of Multiple Gaussian Graphical Models
graphical models structured sparsity alternating direction method of multipliers gene regulatory networks lasso multivariate normal
2013/4/28
We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of ...
We propose a general Bayesian network model for application in a wide class of problems of therapy monitoring. We discuss the use of stochastic simulation as a computational approach to inference on t...
Compressive Shift Retrieval
Compressed sensing shift retrieval sig-nal reconstruction signal registration
2013/5/2
The classical shift retrieval problem considers two signals in vector form that are related by a cyclic shift. In this paper, we develop a compressive variant where the measurement of the signals is u...
The RAppArmor Package: Enforcing Security Policies in R Using Dynamic Sandboxing on Linux
R Security Linux Sandbox AppArmor
2013/5/2
With the increasing availability of public cloud computing facilities and scientific super computers, there is a great potential for making R available through public or shared resources. This allows ...
Performance of the stochastic MV-PURE estimator in highly noisy settings
robust linear estimation reduced-rank estimation stochastic MV-PURE estimator array signal processing
2013/4/28
The stochastic MV-PURE estimator has been developed to provide linear estimation robust to ill-conditioning, high noise levels, and imperfections in model knowledge. In this paper, we investigate the ...