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Variable Selection by Perfect Sampling [PDF]
EURASIP Journal on Advances in Signal Processing, 2002 Variable selection is very important in many fields, and for its resolution many procedures have been proposed and investigated. Among them are Bayesian methods that use Markov chain Monte Carlo (MCMC) sampling algorithms.
Huang Yufei, Djurić Petar M
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High-dimensional variable selection
The Annals of Statistics, 2009 This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods.
Roeder, Kathryn, Wasserman, Larry
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Variable Priority for Unsupervised Variable Selection. [PDF]
Pattern RecognitIn unsupervised settings where labeled data is unavailable, identifying informative features is both challenging and essential. Although numerous methods for unsupervised feature selection have been proposed, significant opportunities for improvement remain.
Zhou L, Lu M, Ishwaran H.
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Variable Selection for Clustering and Classification [PDF]
Journal of Classification, 2013 As data sets continue to grow in size and complexity, effective and efficient techniques are needed to target important features in the variable space. Many of the variable selection techniques that are commonly used alongside clustering algorithms are based upon determining the best variable subspace according to model fitting in a stepwise manner ...
Jeffrey L. Andrews, Paul D. McNicholas
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CoRR, 2014 Variable selection for sparse linear regression is the problem of finding, given an m x p matrix B and a target vector y, a sparse vector x such that Bx approximately equals y. Assuming a standard complexity hypothesis, we show that no polynomial-time algorithm can find a k'-sparse x with ||Bx-y||^2<=h(m,p), where k'=k*2^{log^{1-delta} p} and h(m,p)&
Dean P. Foster +2 more
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BMC Medical Research Methodology, 2020 Background Social-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis.
Elizabeth Handorf +3 more
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Input variable selection for forecasting models [PDF]
, 2002 2002 IFAC15th Triennial World Congress, Barcelona, SpainThe selection of input variables plays a crucial role when modelling time series. For nonlinear models there are not well developed techniques such as AIC and other criteria that work with linear ...
Camacho, Eduardo F. +2 more
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Variable Selection in ROC Regression [PDF]
Computational and Mathematical Methods in Medicine, 2013 Regression models are introduced into thereceiver operating characteristic(ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable ...
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Study of Salary Differentials by Gender and Discipline
Statistics and Public Policy, 2017 Although it is 45 years since legislation made gender discrimination on university campuses illegal, salary inequities continue to exist today. The seminal work in studying the existence of salary inequities is that of the American Association of ...
L. Billard
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Stability Selection for Structured Variable Selection
CoRR, 2017 In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives.
George Philipp +2 more
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