Stein's method in high dimensional classification and applications

DOI10.1016/J.CSDA.2014.08.009OpenAlexW1976616254MaRDI QIDQ1623745

Junyong Park, Dohwan Park

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.08.009

zbMATH Keywords

classificationshrinkagesparsityhigh dimensionStein's estimator

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Cites Work

• High-dimensional classification using features annealed independence rules
• Independent rule in classification of multivariate binary data
• From Stein's unbiased risk estimates to the method of generalized cross- validation
• Estimation of the mean of a multivariate normal distribution
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• Persistence of plug-in rule in classification of high dimensional multivariate binary data
• Adapting to Unknown Smoothness via Wavelet Shrinkage
• Ideal spatial adaptation by wavelet shrinkage
• Understanding WaveShrink: variance and bias estimation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data

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