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Adjusting for selection bias in testing multiple families of hypotheses
false discovery rate family-wise error rate hierarchical testing
2011/7/6
In many large multiple testing problems the hypotheses are divided into families. Given the data, families with evidence for true discoveries are selected, and hypotheses within them are tested.
Multiple Hypotheses Testing For Variable Selection
model selection FDR Lasso Bolasso multiple hypotheses testing
2011/7/6
Many methods have been developed to estimate the set of relevant variables in a sparse linear model Y= XB+e where the dimension p of B can be much higher than the length n of Y.
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies
Bayesian variable selection generalized linear models Gaussian processes
2011/7/5
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data.
spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
MCMC P-splines spike-and-slab prior normal-inverse-gamma
2011/6/20
The R package spikeSlabGAM implements Bayesian variable selection, model choice,
and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and
Poisson responses. Its purpose ...
Variable selection with error control: Another look at Stability Selection
Complementary Pairs Stability Selection r-concavity subagging subsampling variable selection
2011/6/20
Stability Selection was recently introduced by Meinshausen and B¨uhlmann (2010) as
a very general technique designed to improve the performance of a variable selection
algorithm. It is based on aggr...
Consistent Model Selection of Discrete Bayesian Networks from Incomplete Data
Discrete Bayesian Networks Consistent Model Incomplete Data node-variables
2011/6/20
A maximum likelihood based model selection of discrete Bayesian
networks is considered. The model selection is performed through scoring
function S, which, for a given network G and n-sample Dn, is ...
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...