Matrix-variate logistic regression with measurement error

Matrix-variate logistic regression with measurement error

Summary 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, s...

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Veröffentlicht in:Biometrika 2021-03, Vol.108 (1), p.83-97
Hauptverfasser: Fang, Junhan, Yi, Grace Y
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Sprache:eng
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EEG
Electroencephalography
Error analysis
Error correction
Mathematical analysis
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Yi, Grace Y
description Summary 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.
doi_str_mv 10.1093/biomet/asaa056
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source Oxford University Press Journals All Titles (1996-Current)
subjects EEG
Electroencephalography
Error analysis
Error correction
Mathematical analysis
title Matrix-variate logistic regression with measurement error
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