Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth-death processes

DOI10.1214/15-AOAS809zbMath1397.62447arXiv1405.4265WikidataQ31010907 ScholiaQ31010907MaRDI QIDQ746645

Robert E. Weiss, Marc A. Suchard, Forrest W. Crawford

Publication date: 28 October 2015

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.4265

zbMATH Keywords

mixture modelcontinuous-time Markov chainBayesian hierarchical modelroundingcoarse dataheaping

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Branching processes (Galton-Watson, birth-and-death, etc.) (60J80)

Related Items (3)

Integer percentages as electoral falsification fingerprints ⋮ Birth/birth-death processes and their computable transition probabilities with biological applications ⋮ Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth-death processes

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