Nonparametric Recursive Method for Generalized Kernel Estimators for Dependent Functional Data

In the present paper, we are concerned with a generalized kernel estimators defined by the stochastic approximation algorithm in the case of dependent functional data. We establish the central limit theorem for the proposed estimators under some mild conditions. We then approach the distribution of...

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Bibliographic Details
Published inSankhya A Vol. 86; no. 1; pp. 392 - 430
Main Author Slaoui, Yousri
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.02.2024
Springer Verlag
Subjects
Mathematics
Mathematics and Statistics
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
62G09
Secondary 65D10
Primary 62G08
dependent functional data
stochastic approximation algorithm
Regression estimation
62L20
Dependent functional data
Stochastic approximation algorithm
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Summary:In the present paper, we are concerned with a generalized kernel estimators defined by the stochastic approximation algorithm in the case of dependent functional data. We establish the central limit theorem for the proposed estimators under some mild conditions. We then approach the distribution of the bias distribution of our estimate by the bootstrapped distribution when it is conditioned by the data using the Kolmogorov distance.
ISSN:0976-836X
0972-7671
0976-8378
0976-836X
DOI:10.1007/s13171-023-00325-7