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Deviance Information Criteria for Model Selection in Approximate Bayesian Computation
Approximate Bayesian computation evolutionary genetics statistical
2011/6/16
Approximate Bayesian computation (ABC) is a class of algorithmic
methods in Bayesian inference using statistical summaries and computer
simulations. ABC has become popular in evolutionary genetics a...
Selection models with monotone weight functions in meta analysis
global constrained optimization meta analysis monotone non-increasing selection bias
2011/3/24
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the res...
Multi-stage Convex Relaxation for Feature Selection
Multi-stage Convex Relaxation Feature Selection
2011/7/5
A number of recent work studied the effectiveness of feature selection using Lasso. It is known that under the restricted isometry properties (RIP), Lasso does not generally lead to the exact recovery...
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
Submodular meets Spectral Greedy Algorithms for Subset Selection Sparse Approximation Dictionary Selection
2011/3/23
We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can be viewed in the cont...
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
Greedy Algorithms Subset Selection Dictionary Selection
2011/3/22
We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can be viewed in the cont...
Predictive Active Set Selection Methods for Gaussian Processes
Gaussian process classifi cation active set selection predictive distribution expectation propagation
2011/3/24
We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists on a two step alter...
Tight conditions for consistent variable selection in high dimensional nonparametric regression
Tight conditions for consistent variable selection high dimensional nonparametric regression
2011/3/23
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the num...
Tight conditions for consistent variable selection in high dimensional nonparametric regression
variable selection high dimensional nonparametric regression
2011/3/22
We address the issue of variable selection in the regression model with very high ambient dimension, i.e., when the number of covariates is very large. The main focus is on the situation where the nu...
Adaptation to anisotropy and inhomogeneity via dyadic piecewise polynomial selection
Adaptation to anisotropy inhomogeneity via dyadic piecewise polynomial selection
2011/3/23
This article is devoted to nonlinear approximation and estimation via piecewise polynomials built on partitions into dyadic rectangles. The approximation rate is studied over possibly inhomogeneous an...
Adaptation to anisotropy and inhomogeneity via dyadic piecewise polynomial selection
anisotropy inhomogeneity piecewise polynomial
2011/3/22
This article is devoted to nonlinear approximation and estimation via piecewise polynomials built on partitions into dyadic rectangles. The approximation rate is studied over possibly inhomogeneous an...
Estimating composite functions by model selection
Curve estimation model selection composite functions
2011/3/21
We consider the problem of estimating a function $s$ on $[-1,1]^{k}$ for large values of $k$ by looking for some best approximation by composite functions of the form $g\circ u$. Our solution is based...
Consistency of Bayesian Linear Model Selection With a Growing Number of Parameters
Bayesian model selection growing number of parameters Posterior model consistency consistency of Bayes factor consistency of posterior odds ratio Gibbs sampling
2011/3/18
Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which...
The goal of cross-domain object matching (CDOM) is to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summarization is a typical application of...
Model Selection by Loss Rank for Classification and Unsupervised Learning
Classification graphical models loss rank principle model selection
2010/11/9
Hutter (2007) recently introduced the loss rank principle (LoRP) as a general-purpose principle for model selection. The LoRP enjoys many attractive prop-erties and deserves further investigations. Th...
The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Model selection lasso loss rank principle shrinkage parameter variable se-lection
2010/11/9
Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularizatio...