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- W3044767568 abstract "We combine the k ‐Nearest Neighbors ( k NN) method to the local linear estimation (LLE) approach to construct a new estimator (LLE‐ k NN) of the regression operator when the regressor is of functional type and the response variable is a scalar but observed with some missing at random (MAR) observations. The resulting estimator inherits many of the advantages of both approaches ( k NN and LLE methods). This is confirmed by the established asymptotic results, in terms of the pointwise and uniform almost complete consistencies, and the precise convergence rates. In addition, a numerical study (i) on simulated data, then (ii) on a real dataset concerning the sugar quality using fluorescence data, were conducted. This practical study clearly shows the feasibility and the superiority of the LLE‐ k NN estimator compared to competitive estimators." @default.
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- W3044767568 date "2020-10-15" @default.
- W3044767568 modified "2023-10-06" @default.
- W3044767568 title "<i>k</i>‐Nearest neighbors local linear regression for functional and missing data at random" @default.
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- W3044767568 doi "https://doi.org/10.1111/stan.12224" @default.
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