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Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors
Bayes factor Conditional Predictive Ordinate Conjugate prior Poisson regression Logistic regression
2009/9/22
In this paper, we consider theoretical and computational connections
between six popular methods for variable subset selection in generalized linear
models (GLMs) Under the conjugate priors develope...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
feature selection optimistic bias naive Bayes models gene expression data
2009/9/22
For many classication and regression problems, a large number of
features are available for possible use this is typical of DNA microarray data
on gene expression, for example. Often, for computatio...
Discrete time portfolio selection with proportional transaction cost
PortfoIio selection Transaction costs Bellman equation
2009/9/22
In the paper discrete time portblio selection with
maximization of a discounted satisfaction functional is studied. In Section
2 the case without transaction costs is considered and explint
solutio...
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...
Honest variable selection in linear and logistic regression models via $ell_1$ and $ell_1 + ell_2$ penalization
penalty sparse consistent variable selection regression generalized linear models logistic regression
2009/9/16
This paper investigates correct variable selection in finite samples via $ell_1$ and $ell_1 + ell_2$ type penalization schemes. The asymptotic consistency of variable selection immediately follows fro...
LASSO, Iterative Feature Selection and the Correlation Selector: Oracle inequalities and numerical performances
Regression estimation statistical learning confidence regions shrinkage and thresholding methods LASSO
2009/9/16
We propose a general family of algorithms for regression estimation with quadratic loss, on the basis of geometrical considerations. These algorithms are able to select relevant functions into a large...
Adaptive estimation of linear functionals by model selection
Nonparametric regression white noise model adaptive estimation model selection pointwise adaptive estimation
2009/9/16
We propose an estimation procedure for linear functionals based on Gaussian model selection techniques. We show that the procedure is adaptive, and we give a non asymptotic oracle inequality for the r...
Estimation of Gaussian graphs by model selection
Gaussian graphical model Random matrices Model selection Penalized empirical risk
2009/9/16
We investigate in this paper the estimation of Gaussian graphs by model selection from a non-asymptotic point of view. We start from a $n$-sample of a Gaussian law $mathbb{P}_C$ in $mathbb{R}^p$ and f...
Variable selection for multicategory SVM via adaptive sup-norm regularization
Classification L1-norm penalty multicategory sup-norm SVM
2009/9/16
Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatica...
Structured variable selection in support vector machines
Classification Heredity Nonparametric estimation Support vector machine Variable selection
2009/9/16
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The ...
Nonparametric Bayesian model selection and averaging
Adaptation rate of convergence Bayes factor rate of contraction
2009/9/16
We consider nonparametric Bayesian estimation of a probability density p based on a random sample of size n from this density using a hierarchical prior. The prior consists, for instance, of prior wei...
Admissible, consistent multiple testing with applications including variable selection
backward method exponential family forward method step-down procedures step-up procedures
2009/9/16
For multivariate normal models and some exponential family models a multiple testing stepwise method is offered that is both admissible and consistent. The method is readily adaptable to selecting var...
Model selection by resampling penalization
Non-parametric statistics resampling exchangeable weighted bootstrap model selection penalization non-parametric regression
2009/9/16
In this paper, a new family of resampling-based penalization procedures for model selection is defined in a general framework. It generalizes several methods, including Efron's bootstrap penalization ...
Thresholding-based iterative selection procedures for model selection and shrinkage
Sparsity nonconvex penalties thresholding model selection & shrinkage lasso ridge SCAD
2009/9/16
This paper discusses a class of thresholding-based iterative selection procedures (TISP) for model selection and shrinkage. People have long before noticed the weakness of the convex $l_1$-constraint ...