Nonparametric Relative Error Estimation via Functional Regressor by the k Nearest Neighbors Smoothing Under Truncation Random Data
The relation between a functional random covariate and a scalar answer due to left truncation by a different random variable is evaluated in this study with the kNN method. In particular, in order to produce a nonparametric kNN regression operator of these functional truncated data as a loss function, we should use mean squared relative error. In number of neighbors, we establish an estimator and assess the uniform consistency performance with the convergence rate. Then, for different levels of computational truncated data, a simulation analysis was carried out on finite-sized samples to show the feasibility of our estimation procedure and to highlight its superiority to traditional kernel estimation.
Nonparametric Relative Error Estimation via Functional Regressor by the k Nearest Neighbors Smoothing Under Truncation Random Data,
Applications and Applied Mathematics: An International Journal (AAM), Vol. 16,
1, Article 6.
Available at: https://digitalcommons.pvamu.edu/aam/vol16/iss1/6