Testing conditional quantile independence with functional covariate.
Biometrics
; 80(2)2024 Mar 27.
Article
em En
| MEDLINE
| ID: mdl-38742907
ABSTRACT
We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
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Método de Monte Carlo
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Modelos Estatísticos
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China