A1 Journal article (refereed)
Bayesian Modeling of Sequential Discoveries (2022)

Zito, A., Rigon, T., Ovaskainen, O., & Dunson, D. B. (2022). Bayesian Modeling of Sequential Discoveries. Journal of the American Statistical Association, Early online. https://doi.org/10.1080/01621459.2022.2060835

JYU authors or editors

Publication details

All authors or editors: Zito, Alessandro; Rigon, Tommaso; Ovaskainen, Otso; Dunson, David B.

Journal or series: Journal of the American Statistical Association

ISSN: 0162-1459

eISSN: 1537-274X

Publication year: 2022

Publication date: 11/04/2022

Volume: Early online

Publisher: Taylor & Francis

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1080/01621459.2022.2060835

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84100

Abstract

We aim at modelling the appearance of distinct tags in a sequence of labelled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarised via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modelling by directly specifying the probability of a new discovery, therefore allowing for flexible specifications. The asymptotic behavior and finite sample properties of such an approach are extensively studied. Interestingly, our enlarged class of sequential processes includes highly tractable special cases. We present a subclass of models characterized by appealing theoretical and computational properties, including one that shares the same discovery probability with the Dirichlet process. Moreover, due to strong connections with logistic regression models, the latter subclass can naturally account for covariates. We finally test our proposal on both synthetic and real data, with special emphasis on a large fungal biodiversity study in Finland.

Keywords: statistical models; Bayesian analysis; sampling (statistical methods); species survey

Free keywords: accumulation curves; dirichlet process; logistic regression; poisson-binomial distribution; species sampling models

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 3