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Exploiting Disagreement Between High-Dimensional Variable Selectors for Uncertainty Visualization

We propose combined selection and uncertainty visualizer (CSUV), which visualizes selection uncertainties for covariates in high-dimensional linear regression by exploiting the (dis)agreement among different base selectors. Our proposed method highlights covariates that get selected the most frequently by the different base variable selection methods on subsampled data. The method is generic and can be used with different existing variable selection methods. We demonstrate its performance using real and simulated data. The corresponding R package CSUV is at https://github.com/christineyuen/CSUV, and the graphical tool is also available online via https://csuv.shinyapps.io/csuv.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.31 No.2 June 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 351-359

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

VARIABLE SELECTION
HIGH-DIMENSIONAL DATA
UNCERTAINTY VISUALIZATION

Info Detail Spesifik

https://doi.org/10.1080/10618600.2021.2000421

Pernyataan Tanggungjawab

Christine Yuen, Piotr Fryzlewicz

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