An efficient model-free approach to interaction screening for high dimensional data.
Stat Med
; 42(10): 1583-1605, 2023 05 10.
Article
em En
| MEDLINE
| ID: mdl-36857779
ABSTRACT
An innovated model-free interaction screening procedure called the MCVIS is proposed for high dimensional data analysis. Specifically, we adopt the introduced MCV index for quantifying the importance of an interaction effect among predictors. Our proposed method is fully nonparametric and is capable of successfully selecting interactions even if the signal of parental main effects is weak. The MCVIS procedure has many distinctive features (i) it can work with discrete, categorical and continuous covariates; (ii) it can deal with both categorical and continuous response, even handle the missing response; (iii) it is robust for heavy-tailed distributions, thus well accommodates heterogeneity typically caused by high dimensionality; (iv) it enjoys the sure screening and ranking consistency properties, therefore achieves dimension reduction without information loss. In another respect, computational feasibility is a top concern in high dimensional data analysis, by transforming our MCV into several variants, the MCVIS procedure is simple and fast to implement. Extensive numerical experiments and comparisons confirm the effectiveness and wide applicability of our MCVIS procedure. We further illustrate the proposed methodology by empirical study of two real datasets. Supplementary materials are available online.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise de Dados
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article