A robust functional time series forecasting method

DOI10.1080/00949655.2019.1572146OpenAlexW2909201101MaRDI QIDQ5107356

Han Lin Shang

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.06030

zbMATH Keywords

projection pursuitvector autoregressive modelground-level ozone concentrationmultivariate least trimmed squares estimatorsrobust functional principal component regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Linear regression; mixed models (62J05) Applications of statistics to environmental and related topics (62P12)

Related Items

Forecasting functional time series using weighted likelihood methodology

Uses Software

• MCS

Cites Work

• Unnamed Item
• Unnamed Item
• The Model Confidence Set
• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Robust forecasting of mortality and fertility rates: a functional data approach
• Robust functional linear regression based on splines
• Robust estimation for vector autoregressive models
• Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
• The multivariate least-trimmed squares estimator
• A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis
• Measuring the Advantages of Multivariate vs. Univariate Forecasts
• An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
• Alternatives to the Median Absolute Deviation
• Estimation of the Mean of Functional Time Series and a Two-Sample Problem
• A Plug‐in Bandwidth Selection Procedure for Long‐Run Covariance Estimation with Stationary Functional Time Series
• On the Prediction of Stationary Functional Time Series
• Dynamic Functional Principal Components
• Robust Statistics
• A Simplex Method for Function Minimization