On Nonparametric Conditional Quantile Estimation for Non-stationary Random
Keyword(s):
A kernel conditional quantile estimate of a real-valued non-stationary spatial process is proposed for a prediction goal at a non-observed location of the underlying process. The originality is based on the ability to take into account some local spatial dependency. Large sample properties based on almost complete and \(L^q\)-consistencies of the estimator are established. A numerical study is given in order to illustrate the performance of our methodology.
2021 ◽
Vol 164
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pp. 120566
2015 ◽
Vol 36
(6)
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pp. 969-990
2017 ◽
Vol 201
(1)
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pp. 72-94
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2014 ◽
Vol 33
(2)
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pp. 167-178
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2015 ◽
Vol 156
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pp. 14-30
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