Matrix-variate logistic regression with measurement error
成果类型：
Article
署名作者：
Fang, Junhan; Yi, Grace Y.
署名单位：
University of Waterloo; Western University (University of Western Ontario)
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asaa056
发表日期：
2021
页码：
8397
关键词：
摘要：
Measurement error in covariates has been extensively studied in many conventional regression settings where covariate information is typically expressed in a vector form. However, there has been little work on error-prone matrix-variate data, which commonly arise from studies with imaging, spatial-temporal structures, etc. We consider analysis of error-contaminated matrix-variate data. We particularly focus on matrix-variate logistic measurement error models. We examine the biases induced from naive analysis which ignores measurement error in matrix-variate data. Two measurement error correction methods are developed to adjust for measurement error effects. The proposed methods are justified both theoretically and empirically. We analyse an electroencephalography dataset with the proposed methods.
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