Estimating invariant laws of linear processes by \(U\)-statistics.

DOI10.1214/009053604000000111zbMath1091.62066arXivmath/0406431OpenAlexW1965436372MaRDI QIDQ1879946

Anton Schick, Wolfgang Wefelmeyer

Publication date: 15 September 2004

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0406431

zbMATH Keywords

time seriessemiparametric modelefficient estimatorplug-in estimatorleast dispersed estimatorconstrained model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09)

Related Items (4)

Root n consistent and optimal density estimators for moving average processes ⋮ Efficient prediction for linear and nonlinear autoregressive models ⋮ Some developments in semiparametric statistics ⋮ Improved estimators for constrained Markov chain models

Cites Work

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• Estimating the innovation distribution in nonlinear autoregressive models
• Efficiency of empirical estimators for Markov chains
• On the estimation of the marginal density of a moving average process
• Estimation of the Distribution of Noise in an Autoregression Scheme
• Approximation Theorems of Mathematical Statistics
• On estimation and adaptive estimation for locally asymptotically normal families
• Root n consistent and optimal density estimators for moving average processes
• Kernel density estimation for linear processes
• Nonparametric density estimation for a long-range dependent linear process
• Improved estimators for constrained Markov chain models
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