A1 Journal article (refereed)
Bayesian semiparametric long memory models for discretized event data (2022)
Chakraborty, A., Ovaskainen, O., & Dunson, D. B. (2022). Bayesian semiparametric long memory models for discretized event data. Annals of Applied Statistics, 16(3), 1380-1399. https://doi.org/10.1214/21-AOAS1546
JYU authors or editors
Publication details
All authors or editors: Chakraborty, Antik; Ovaskainen, Otso; Dunson, David B.
Journal or series: Annals of Applied Statistics
ISSN: 1932-6157
eISSN: 1941-7330
Publication year: 2022
Publication date: 19/07/2022
Volume: 16
Issue number: 3
Pages range: 1380-1399
Publisher: Institute of Mathematical Statistics
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1214/21-AOAS1546
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/82484
Publication is parallel published: https://arxiv.org/abs/2004.08309
Abstract
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent process is modeled by a smooth Gaussian process and a fractional Brownian motion by assuming an additive structure. We develop a Bayesian approach to inference using Markov chain Monte Carlo and show good performance in simulation studies. Applying the methods to the Amazon bird vocalization data, we find substantial evidence for self-similarity and non-Markovian/Poisson dynamics. To accommodate the bird vocalization data in which there are many different species of birds exhibiting their own vocalization dynamics, a hierarchical expansion of FRAP is provided in the Supplementary Material.
Keywords: modelling (representation); fractals; Bayesian analysis; Monte Carlo methods; Markov chains; Gaussian processes; time series; mathematical statistics; probability calculation; bird sounds; nature sounds; natural diversity; rain forests
Free keywords: fractal; fractional Brownian motion; latent Gaussian process models; long range dependence; nonparametric Bayes; probit; time series
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 3