A Unified View of Nonparametric Trend-Cycle Predictors Via Reproducing Kernel Hilbert Spaces

DOI10.1080/07474938.2012.690674zbMath1491.62093OpenAlexW2089516014MaRDI QIDQ5080579

Estela Bee Dagum, Silvia Bianconcini

Publication date: 31 May 2022

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474938.2012.690674

zbMATH Keywords

spectral propertiesrevisionssmoothing cubic splinespolynomial kernel regressionreal time analysis

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10)

Related Items

Monitoring the direction of the short-term trend of economic indicators

Cites Work

• Spline smoothing: The equivalent variable kernel method
• Spline functions and stochastic filtering
• On a generalization of the logistic distribution
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Hierarchies of higher order kernels
• Regularization networks and support vector machines
• On characterizations of the logistic distribution
• Robust Locally Weighted Regression and Smoothing Scatterplots
• A remark on the shape of the logistic distribution
• Applications: The Surrogate Henderson Filters in X‐11
• Quantile Regression in Reproducing Kernel Hilbert Spaces
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• Theory of Reproducing Kernels
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item