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A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data

delete2026-03-01
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PRE
AI
M
Matthew D. Koslovsky *
A
Andee Kaplan
V
Victoria A. Terranova
M
Mevin B. Hooten
DOI：10.1214/24-BA1477delete
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Abstract

Abstract

En 中文
Measurement error in multinomial data is a well-known and well-studied inferential problem that is encountered in many fields, including engineering, biomedical and omics research, ecology, finance, official statistics, and social sciences. Methods developed to accommodate measurement error in multinomial data are typically equipped to handle false negatives or false positives, but not both. We provide a unified framework for accommodating both forms of measurement error using a Bayesian hierarchical approach. We demonstrate the proposed method's performance on simulated data and apply it to acoustic bat monitoring and official crime data.
Keywords:
categorical data
criminology
ecology
misclassification
record linkage
zero-inflation

Journal

Bayesian Analysis cover
Bayesian Analysis
IF:
2.5
Papers:
34
Citations:
3.0K

Organization

No organization information available