Bayesian semiparametric long memory models for discretized event data
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
Julkaistu sarjassa
Annals of Applied StatisticsPäivämäärä
2022Tekijänoikeudet
© 2022 Institute of Mathematical Statistics
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.
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Julkaisija
Institute of Mathematical StatisticsISSN Hae Julkaisufoorumista
1932-6157Asiasanat
ekologinen mallinnus Amazon fractal fractional Brownian motion latent Gaussian process models long range dependence nonparametric Bayes probit time series fraktaalit todennäköisyyslaskenta bayesilainen menetelmä mallintaminen matemaattinen tilastotiede gaussiset prosessit Markovin ketjut aikasarjat luonnon monimuotoisuus Monte Carlo -menetelmät luonnonäänet linnut -- äänet sademetsät
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https://converis.jyu.fi/converis/portal/detail/Publication/150929705
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The authors acknowledge support from the United States Office of Naval Research (ONR) and the European Research Council (ERC).Lisenssi
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