Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods

DOI10.1080/01621459.2013.858630zbMath1367.62219OpenAlexW2090524995WikidataQ40993588 ScholiaQ40993588MaRDI QIDQ4975400

Leonard A. Stefanski, Yichao Wu, Kyle White

Publication date: 4 August 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4066561

zbMATH Keywords

attenuationconvolutionmodel selectiondiscriminant analysisridge regressionlinear regressionbinary regressionLassoBayes rulekernel discriminant analysismaximum likelihood rule

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Efficient kernel-based variable selection with sparsistency, Statistical inference on restricted linear regression models with partial distortion measurement errors, Nonparametric independence screening via favored smoothing bandwidth, Nearest neighbor estimates of regression, A General Framework of Nonparametric Feature Selection in High-Dimensional Data, Variable selection for nonparametric quantile regression via measurement error model, Variable Selection for Global Fréchet Regression, Automatic structure recovery for generalized additive models, High-dimensional local linear regression under sparsity and convex losses, Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions, Univariate measurement error selection likelihood for variable selection of additive model

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