A note on the statistical properties of nonparametric trend estimators by means of smoothing matrices

DOI10.1080/10485250802534986zbMath1155.62065OpenAlexW1986182910MaRDI QIDQ3611826

Estela Bee Dagum, Alessandra Luati

Publication date: 3 March 2009

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250802534986

zbMATH Keywords

time-series decompositionlinear filterssmoothing matriceslocal polynomial regression methods

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Basic linear algebra (15A99)

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Cites Work

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• Linear smoothers and additive models
• Consistent nonparametric regression. Discussion
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• A linear transformation and its properties with special applications in time series filtering
• Smoothing by spline functions.
• Robust Locally Weighted Regression and Smoothing Scatterplots
• A Seasonal Adjustment Principle and a Seasonal Adjustment Method Derived from this Principle
• SEASONAL ADJUSTMENT BY A BAYESIAN MODELING
• A completely automatic french curve: fitting spline functions by cross validation
• Classroom Note:Centrosymmetric Matrices
• Curve Estimates
• Seasonal adjustment with the X-11 method

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