Non‐parametric smoothing and prediction for nonlinear circular time series

DOI10.1111/j.1467-9892.2012.00794.xzbMath1281.62194OpenAlexW1495158260WikidataQ59902397 ScholiaQ59902397MaRDI QIDQ5397938

Agnese Panzera, Charles C. Taylor, MacRo di Marzio

Publication date: 25 February 2014

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: http://eprints.whiterose.ac.uk/74708/6/TaylorCsubmitted.pdf

zbMATH Keywords

long-range dependenceshort-range dependencekernel weightslinear weightsangular risk

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05)

Related Items

On nonparametric regression for bivariate circular long-memory time series ⋮ Recent advances in directional statistics ⋮ Kernel regression for errors-in-variables problems in the circular domain ⋮ Estimating the Mean Direction of Strongly Dependent Circular Time Series ⋮ Non‐parametric Regression for Circular Responses ⋮ Nonparametric dynamic state space modeling of observed circular time series with circular latent states: a Bayesian perspective ⋮ Bayesian nonparametric dynamic state space modeling with circular latent states ⋮ Smooth estimation of circular cumulative distribution functions and quantiles

Cites Work

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• Nonlinear time series. Nonparametric and parametric methods
• Nonlinear Time Series
• Kernel Regression Estimation Using Repeated Measurements Data
• Understanding Exponential Smoothing Via Kernel Regression
• Circular regression
• Analysis of Circular Longitudinal Data Based on Generalized Estimating Equations

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