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 editorsChakraborty, Antik; Ovaskainen, Otso; Dunson, David B.

Journal or seriesAnnals of Applied Statistics

ISSN1932-6157

eISSN1941-7330

Publication year2022

Publication date19/07/2022

Volume16

Issue number3

Pages range1380-1399

PublisherInstitute of Mathematical Statistics

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1214/21-AOAS1546

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/82484

Publication is parallel publishedhttps://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.


Keywordsmodelling (representation)fractalsBayesian analysisMonte Carlo methodsMarkov chainsGaussian processestime seriesmathematical statisticsprobability calculationbird soundsnature soundsnatural diversityrain forests

Free keywordsfractal; fractional Brownian motion; latent Gaussian process models; long range dependence; nonparametric Bayes; probit; time series


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating3


Last updated on 2024-25-03 at 10:46