Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method

Salim Bouzebda; Yousri Slaoui

doi:10.1016/j.spl.2019.03.007

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Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method

Salim Bouzebda Yousri Slaoui

Year: 2019 Journal: Statistics & Probability Letters Vol: 151 Pages: 17-28 Publisher: Elsevier BV

DOI: 10.1016/j.spl.2019.03.007

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Keywords:

Mathematics Estimator Kernel (algebra) Kernel regression Kernel method Regression function Regression Kernel density estimation Applied mathematics Statistics Function (biology) Regression analysis Artificial intelligence Computer science Combinatorics Support vector machine

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Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Soil Geostatistics and Mapping

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Mathematical Approximation and Integration

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Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method

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