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Online Learning in a Contract Selection Problem
Online Learning Contract Selection Problem
2013/6/14
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitab...
Feature Selection Based on Term Frequency and T-Test for Text Categorization
feature selection term frequency t-test text classification
2013/6/14
Much work has been done on feature selection. Existing methods are based on document frequency, such as Chi-Square Statistic, Information Gain etc. However, these methods have two shortcomings: one is...
Selection of Identifiability Criteria for Total Effects by using Path Diagrams
Selection Identifiability Criteria Total Effects Path Diagrams
2012/9/19
Pearl has provided the back door criterion,the front door criterion and the conditional instrumental variable (IV) method as iden-tifiability criteria for total effects.In some situations,these three ...
Unified Analysis of Transmit Antenna Selection/Space-Time Block Coding with Receive Selection and Combining over Nakagami-m Fading Channels in the Presence of Feedback Errors
Space-Time Block Coding (STBC) Transmit Antenna Selection (TAS) Receive Antenna Selection (RAS) Maximal-ratio Combining (MRC) Selection Combining (SC) Nakagami-m fading Feedback Errors
2012/9/18
Examining the effect of imperfect transmit antenna selection (TAS) caused by the feedback link errors on the performance of hybrid TAS/space-time block co ding (STBC) with selection combining (SC) (i....
Variable Selection with Exponential Weights and $l_0$-Penalization
Variable selection model selection sparse linear model xponential weights Gibbs sampler identifiability condition.
2012/9/17
In the context of a linear model with a sparse coefficient vector, exponential weights methods have been shown to be achieve oracle inequalities for prediction. We show that such methods also succeed ...
Simultaneous Model Selection and Estimation for Mean and Association Structures with Clustered Binary Data
association clustered binary data generalized estimating equation logistic regression variable selection
2012/9/17
This paper investigates the property of the penalized estimating equations when both the mean and association structures are modelled. To select variables for the mean and association structures seque...
The Dependence of Routine Bayesian Model Selection Methods on Irrelevant Alternatives
Bayesian Model Selection Methods Alternatives
2012/9/17
Bayesian methods - either based on Bayes Factors or BIC - are now widely used for model selection. One property that might reasonably be demanded of any model
selection method is that if a modelM1 is...
Consistent selection of tuning parameters via variable selection stability
kappa coefficient penalized regression selection consistency stability tuning
2012/9/17
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
Oracle inequalities for computationally adaptive model selection
Oracle computationally adaptive model selection
2012/9/17
We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspe...
Gaussian Oracle Inequalities for Structured Selection in Non-Parametric Cox Model
Gaussian Oracle Inequalities Structured Selection Non-Parametric Cox Model
2012/9/19
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard乫s model. Due to high impact of sequencing data, carrying genet...
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...
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...
Error Prediction and Model Selection via Unbalanced Expander Graphs
Error Prediction Model Selection Unbalanced Expander Graphs
2010/10/19
We investigate deterministic design matrices for the fundamental problems of error prediction and model selection. Our deterministic design matrices are constructed from unbalanced expander graphs, a...
Stochastic model selection for Mixtures of Matrix-Normals
Mixture models birth and death process Gibbs sampler
2010/10/19
Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal ...
Selection of variables and dimension reduction in high-dimensional non-parametric regression
dimension reduction high dimension LASSO
2009/9/16
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...