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
Published inAnnals of Applied Statistics
© 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. ...
PublisherInstitute of Mathematical Statistics
ISSN Search the Publication Forum1932-6157
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
Publication in research information system
MetadataShow full item record
Additional information about fundingThe authors acknowledge support from the United States Office of Naval Research (ONR) and the European Research Council (ERC).
Showing items with similar title or keywords.
Conditional particle filters with diffuse initial distributions Karppinen, Santeri; Vihola, Matti (Springer, 2021)Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which ...
Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R Helske, Jouni (Elsevier BV, 2022)The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as ...
Statistical models and inference for spatial point patterns with intensity-dependent marks Myllymäki, Mari (University of Jyväskylä, 2009)
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions Chada, Neil K.; Franks, Jordan; Jasra, Ajay; Law, Kody J.; Vihola, Matti (Society for Industrial & Applied Mathematics (SIAM), 2021)We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretization bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation ...
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction Vihola, Matti; Franks, Jordan (Oxford University Press, 2020)Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation ...