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The variable selection problem
 Journal of the American Statistical Association
, 2000
"... The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables ..."
Abstract

Cited by 39 (2 self)
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The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. This vignette reviews some of the key developments which have led to the wide variety of approaches for this problem. 1
From Bernoulli–Gaussian Deconvolution to Sparse Signal Restoration
"... © 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other w ..."
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Cited by 9 (4 self)
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© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Abstract—Formulated as a least square problem under an 0 constraint, sparse signal restoration is a discrete optimization problem, known to be NP complete. Classical algorithms include, by increasing cost and efficiency, matching pursuit (MP), orthogonal matching pursuit (OMP), orthogonal least squares (OLS), stepwise regression algorithms and the exhaustive search. We revisit the single most likely replacement (SMLR) algorithm, developed in the mid1980s for Bernoulli–Gaussian signal restoration. We show that the formulation of sparse signal restoration as a limit case of Bernoulli–Gaussian signal restoration leads to an 0penalized least square minimization problem, to which SMLR can be straightforwardly adapted. The resulting algorithm, called single best replacement (SBR), can be interpreted as a forward–backward extension of OLS sharing similarities with stepwise regression algorithms. Some structural properties of SBR are put forward. A fast and stable implementation is proposed. The approach is illustrated on two inverse problems involving highly correlated dictionaries. We show that SBR is very competitive with popular sparse algorithms in terms of tradeoff between accuracy and computation time. Index Terms—BernoulliGaussian (BG) signal restoration, inverse problems, mixed 2 0 criterion minimization, orthogonal least squares, SMLR algorithm, sparse signal estimation, stepwise regression algorithms. I.
Analysis of New Variable Selection Methods for Discriminant Analysis
, 2006
"... Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as reducing the ..."
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Cited by 4 (1 self)
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Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as reducing the costs of data acquisition, better understanding of the final classification model, and an increase in the efficiency and efficacy of the model itself. The specific problem consists in finding, for a small integer value of p, the size p subset of original variables that yields the greatest percentage of hits in the discriminant analysis. To solve this problem a series of techniques based on metaheuristic strategies is proposed. After performing some test it is found that they obtain significantly better results than the Stepwise, Backward or Forward methods used by classic statistical packages. The way these methods work is illustrated with several examples.
Growth Empirics without Parameters ∗
, 2010
"... Recent research on growth empirics has been focused on resolving model and variable uncertainty. The conventional approach has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine crosscountry growth. This paper questions the linearity ..."
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Cited by 1 (1 self)
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Recent research on growth empirics has been focused on resolving model and variable uncertainty. The conventional approach has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine crosscountry growth. This paper questions the linearity assumption underlying the vast majority of such research and uses recentlydeveloped nonparametric techniques to consider nonlinearities and variable selection jointly. We show that inclusion of nonlinearities is necessary for determining the empirically relevant variables and uncovering key mechanisms of the growth process. We also show how nonparametric methods can sometimes point towards the correct parametric specification. Each of these points are demonstrated by considering specific growth theories.
A Variable Selection Method based in Tabu Search for Logistic Regression Models
"... A Tabu Search method to select variables that are subsequently used in Logistic Regression Models is proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as redu ..."
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Cited by 1 (0 self)
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A Tabu Search method to select variables that are subsequently used in Logistic Regression Models is proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as reducing the costs of data acquisition, better understanding of the final classification model, and an increase in the efficiency and efficacy of the model itself. The specific problem consists in finding, for a small integer value of p, the size p subset of original variables that yields the greatest percentage of hits in Logistic Regression. To solve this problem a technique based on the metaheuristic strategy Tabu Search is proposed. After performing some tests it is found that it obtains significantly better results than the Stepwise, Backward or Forward methods used by classic statistical packages. The way these methods work is illustrated with several examples.
Measuring the Reliability of the Average Estimated Variance
"... In this dissertation an attempt is made to provide tools for evaluating the reliability of decisions based on the AEV for model selection in the general linear model setting. criterion The traditional distribution theory approach to such an evaluation is shown to be intractable due to the complex na ..."
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In this dissertation an attempt is made to provide tools for evaluating the reliability of decisions based on the AEV for model selection in the general linear model setting. criterion The traditional distribution theory approach to such an evaluation is shown to be intractable due to the complex nature of the joint distribution of the AEV's. A perturbation/conditional risk approach is developed which utilizes the idea of perturbing the observed data and determining the proportion of decisions based on perturbed data which differ from the decision based on the original data. The,proportion of changed decisions is shown to be a conditional risk function for an appropriately defined loss function.
ThreadMarks: A Framework for InputAware Prediction of Parallel Application Behavior
"... Chipmultiprocessors (CMPs) are quickly becoming entrenched as the mainstream architectural platform in computer systems. One of the critical challenges facing CMPs is designing applications to effectively leverage the computational resources they provide. Modifying applications to effectively run ..."
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Chipmultiprocessors (CMPs) are quickly becoming entrenched as the mainstream architectural platform in computer systems. One of the critical challenges facing CMPs is designing applications to effectively leverage the computational resources they provide. Modifying applications to effectively run on CMPs requires understanding the bottlenecks in applications, which necessitates a detailed understanding of architectural features. Unfortunately, identifying bottlenecks is complex and often requires enumerating a wide range of behaviors. To assist in identifying bottlenecks, this paper presents a framework for developing analytical models based on dynamic program behaviors. That is, given a program and set of training inputs, the framework will generate several analytical models that accurately predict online program behaviors such as memory utilization and synchronization overhead, while taking program input into consideration. These models can prove invaluable for online optimization systems and inputspecific analysis of program behavior. We demonstrate that this framework is practical and accurate on a wide range of synthetic and realworld parallel applications over various workloads. 1
Frequency
"... independent automatic input variable selection for neural networks for forecasting ..."
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independent automatic input variable selection for neural networks for forecasting
Organization of the Documentation.......................................................................................... xi
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Statistical Estimation of Precipitable Water With SIRSB Water Vapor Radiation Measurements
"... ABSTRACTA multipleparameter model has been for precipitable water above the 1000mb level was approximulated to estimate precipitable water profiles above the mately 20 percent. The,532cm1 water vapor channel standard pressure levels from the satellite infrared spectrom alone explained 72 perc ..."
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ABSTRACTA multipleparameter model has been for precipitable water above the 1000mb level was approximulated to estimate precipitable water profiles above the mately 20 percent. The,532cm1 water vapor channel standard pressure levels from the satellite infrared spectrom alone explained 72 percent of the variance of thc preeter B (SIRSB) radiation observations taken from the cipitable water. This method was used to specify thc Nimbus 4 satellite. The method was verified with coinci optimum SIRSB spectral intervals for future water dent radiosonde data. The relative error of SIRSderived vapor sounding. 1.