On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators

DOI10.1007/BF02482541zbMath0612.62126OpenAlexW2156182013MaRDI QIDQ1088355

Peter M. Robinson

Publication date: 1986

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02482541

zbMATH Keywords

kernel estimatorsweak consistencyconditional expectationsnonparametric regressionautoregressionmixing conditionsdensity estimatorschoice of bandwidthmultivariate stationary time seriesfinite-sample propertiesjoint probability densities

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05)

Related Items

Consistent estimation of a general nonparametric regression function in time series, Kernel density estimation for linear processes: Asymptotic normality and optimal bandwidth derivation, Multivariate regression estimation: Local polynomial fitting for time series, Prediction in invertible linear processes, Efficient prediction for linear and nonlinear autoregressive models, Multivariate regression estimation: Local polynomial fitting for time series, Convergence rates in density estimation for data from infinite-order moving average processes, MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES, Nonparametric regression estimation under mixing conditions, Density estimation in \(\mathbb{L}^\infty\) norm for mixing processes, Strong consistency and rates for recursive nonparametric conditional probability density estimates under \((\alpha{}, \beta{})\)-mixing conditions, Prediction in moving average processes, Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes, Nonparametric estimation of conditional probability densities and expectations of stationary processes: Strong consistency and rates, Minimum distance regression-type estimates with rates under weak dependence, Multivariate regression estimation with errors-in-variables for stationary processes

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