Bayesian nonparametric dynamic state space modeling with circular latent states
From MaRDI portal
Publication:2323159
DOI10.1080/15598608.2015.1100562zbMath1420.62390arXiv1408.3041OpenAlexW2109683364MaRDI QIDQ2323159
Satyaki Mazumder, Sourabh Bhattacharya
Publication date: 30 August 2019
Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1408.3041
state space modelMarkov-chain Monte Carlokernel convolutioncircular random variablewrapped Gaussian processWeiner process
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Gaussian processes (60G15)
Related Items (2)
Recent advances in directional statistics ⋮ Nonparametric dynamic state space modeling of observed circular time series with circular latent states: a Bayesian perspective
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Kernel density estimation with spherical data
- Kernel density estimation on the torus
- Local polynomial regression for circular predictors
- On spectral estimation for a homogeneous random process on the circle
- Simple tests for the validity of correlation function models on the circle
- Rotation space random fields with an application to fMRI data
- Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. With comments and a rejoinder by the author
- Bayesian inference in nonparametric dynamic state-space models
- Using small bias nonparametric density estimators for confidence interval estimation
- Non‐parametric Regression for Circular Responses
- Nonparametric Regression for Spherical Data
- Random Fields and Geometry
- 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
This page was built for publication: Bayesian nonparametric dynamic state space modeling with circular latent states
