ArticleThe annals of applied statistics
SEX, LIES AND SELF-REPORTED COUNTS: BAYESIAN MIXTURE MODELS FOR HEAPING IN LONGITUDINAL COUNT DATA VIA BIRTH-DEATH PROCESSES.
Article in The annals of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 30 citations in OpenAlex.
- Coarsened Data in Small Area Estimation: A Bayesian Two-Part Model for Mapping Smoking Behavior.Statistics in medicine · 2026Article
- Epidemic thresholds across sexual behavior groups in the UK Biobank.Frontiers in reproductive health · 2026Article
- Clinical progression parameters associated with SARS-CoV-2, influenza, and respiratory syncytial virus infections in a large US integrated healthcare population.PLoS computational biology · 2025Article
- A method of correction for heaping error in the variables using validation data.Statistical papers (Berlin, Germany) · 2023Article
- Birth/birth-death processes and their computable transition probabilities with biological applications.Journal of mathematical biology · 2018Article
- Computational methods for birth-death processes.Wiley interdisciplinary reviews. Computational statisticsArticle
- SEX, LIES AND SELF-REPORTED COUNTS: BAYESIAN MIXTURE MODELS FOR HEAPING IN LONGITUDINAL COUNT DATA VIA BIRTH-DEATH PROCESSES.The annals of applied statisticsArticle
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3 authors at 1 institution in 1 country.
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Abstract
Surveys often ask respondents to report non-negative counts, but respondents may misremember or round to a nearby multiple of 5 or 10. This phenomenon is called heaping, and the error inherent in heaped self-reported numbers can bias estimation. Heaped data may be collected cross-sectionally or longitudinally and there may be covariates that complicate the inferential task. Heaping is a well-known issue in many survey settings, and inference for heaped data is an important statistical problem. We propose a novel reporting distribution whose underlying parameters are readily interpretable as rates of misremembering and rounding. The process accommodates a variety of heaping grids and allows for quasi-heaping to values nearly but not equal to heaping multiples. We present a Bayesian hierarchical model for longitudinal samples with covariates to infer both the unobserved true distribution of counts and the parameters that control the heaping process. Finally, we apply our methods to longitudinal self-reported counts of sex partners in a study of high-risk behavior in HIV-positive youth.
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