Kernel estimation of the regression function with random sampling times

DOI10.1007/BF02563107zbMath0839.62048OpenAlexW2029778971MaRDI QIDQ1906311

José Antonio Vilar

Publication date: 23 June 1996

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02563107

zbMATH Keywords

asymptotic normalitykernel estimatornonparametric regressionrandom samplingmean integrated square errormixing dependence conditionsstationary continuous-time process

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

Related Items (4)

ON THE CONSISTENCY OF THE LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE RENEWAL CASE ⋮ On the Consistency of Least Squares Estimator in Models Sampled at Random Times Driven by Long Memory Noise: The Jittered Case ⋮ Finite sample performance of density estimators from unequally spaced data ⋮ Least-square estimators in linear regression models under negatively superadditive dependent random observations

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