Nonparametric variable selection and classification: the CATCH algorithm
From MaRDI portal
Publication:1623399
DOI10.1016/J.CSDA.2013.10.024zbMath1506.62175OpenAlexW2013566592MaRDI QIDQ1623399
Shijie Tang, Kam-Wah Tsui, Kjell A. Doksum, Li-Sha Chen
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.2013.10.024
Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Selecting local models in multiple regression by maximizing power
- Analysis of variance, coefficient of determination and \(F\)-test for local polynomial regression
- Improving the precision of classification trees
- Multivariate adaptive regression splines
- Self-normalized Cramér-type large deviations for independent random variables.
- Nonparametric estimation of global functionals and a measure of the explanatory power of covariates in regression
- Variable selection for multicategory SVM via adaptive sup-norm regularization
- Correcting for Population Stratification in Genomewide Association Studies
- OnL1-Norm Multiclass Support Vector Machines
- Bandwidth Selection in Nonparametric Kernel Testing
- Nonparametric Variable Selection: The EARTH Algorithm
- The bootstrap and Edgeworth expansion
- Random forests
This page was built for publication: Nonparametric variable selection and classification: the CATCH algorithm