Tilting Methods for Assessing the Influence of Components in a Classifier
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Publication:2920282
DOI10.1111/j.1467-9868.2009.00701.xzbMath1248.62102OpenAlexW2087892140MaRDI QIDQ2920282
Jing-Hao Xue, Hall, Peter, Michael D. Titterington
Publication date: 16 October 2012
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.1467-9868.2009.00701.x
cross-validationempirical likelihoodvariable selectionhigh dimensional datalassogenomic databiased bootstrapdistance-based classifierscentroid-based classifier
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric statistical resampling methods (62G09)
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On selecting interacting features from high-dimensional data, Principal components adjusted variable screening, Conditional sure independence screening by conditional marginal empirical likelihood, Sure screening by ranking the canonical correlations, Impacts of high dimensionality in finite samples, Feature screening under missing indicator imputation with non-ignorable missing response, A Model-free Variable Screening Method Based on Leverage Score, Quantile-adaptive variable screening in ultra-high dimensional varying coefficient models, Independent feature screening for ultrahigh-dimensional models with interactions, Sure independence screening in generalized linear models with NP-dimensionality, Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
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