Nonparametric dynamic state space modeling of observed circular time series with circular latent states: a Bayesian perspective

DOI10.1080/15598608.2017.1305922zbMath1425.62127arXiv1610.08367OpenAlexW2543112437MaRDI QIDQ2321807

Satyaki Mazumder, Sourabh Bhattacharya

Publication date: 23 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.08367

zbMATH Keywords

state-space modellook-up tableMarkov-chain Monte Carlocircular time serieslatent circular processwrapped Gaussian process

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05) Monte Carlo methods (65C05)

Related Items (max. 100)

Recent advances in directional statistics

Cites Work

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• The analysis of directional time series: Applications to wind speed and direction. (Based on the author's thesis, Univ. of Western Australia in Perth)
• Bayesian inference in nonparametric dynamic state-space models
• Bayesian nonparametric dynamic state space modeling with circular latent states
• Statistical Analysis of Circular Data
• Least circular distance regression for directional data
• Non‐parametric smoothing and prediction for nonlinear circular time series
• Monte Carlo strategies in scientific computing
• A simulation approach to Bayesian emulation of complex dynamic computer models

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