Nonparametric spline regression with autoregressive moving average errors

DOI10.1093/biomet/79.2.335zbMath0751.62017OpenAlexW1971371656MaRDI QIDQ4015839

Craig F. Ansley, Robert Kohn, Chi-ming Wong

Publication date: 29 November 1992

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biomet/79.2.335

zbMATH Keywords

Kalman filtermaximum likelihoodmarginal likelihoodsimulation studystate space modelautoregressive moving average modelgeneralized cross-validationautocorrelated errorsintegrated squared errorfinite sample propertiesspline smoothingcross- validationspline nonparametric regression

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Penalized regression with model-based penalties, Asymptotic normality of spline estimator when the errors are a linear stationary process, Quantiles, expectiles and splines, Joint non-parametric estimation of mean and auto-covariances for Gaussian processes, Dependent error regression smoothing: A new method and PC program, On bandwidth choice in nonparametric regression with both short- and long-range dependent errors, On spline regression under Gaussian subordination with long memory, Accuracy and efficiency of alternative spline smoothing algorithms, Short‐term forecasting with a computationally efficient nonparametric transfer function model, On estimation of nonparametric regression models with autoregressive and moving average errors, Some automated methods of smoothing time-dependent data, Nonparametric regression with correlated errors., On piecewise polynomial regression under general dependence conditions, with an application to calcium-imaging data, The local quadratic trend model, Smoothing non-Gaussian time series with autoregressive structure., Estimation of trend in state-space models: asymptotic mean square error and rate of convergence