Geodesic
On the nonparametric estimation of the functional regression based on censored data under strong mixing condition
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 15 (2022) no. 4, pp. 523-536

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In this paper, we are concerned with local linear nonparametric estimation of the regression function in the censorship model when the covariates take values in a semimetric space. Then, we establish the pointwise almost-complete convergence, with rate, of the proposed estimator when the sample is a strong mixing sequence. To lend further support to our theoretical results, a simulation study is carried out to illustrate the good accuracy of the studied method.
Keywords: functional data, censored data, locally modeled regression, almost-complete convergence, strong mixing. 
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     author = {Farid Leulmi and Sara Leulmi and Soumia Kharfouchi},
     title = {On the nonparametric estimation of the functional regression based on censored data under strong mixing condition},
     journal = {\v{Z}urnal Sibirskogo federalʹnogo universiteta. Matematika i fizika},
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     year = {2022},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JSFU_2022_15_4_a10/}
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Farid Leulmi; Sara Leulmi; Soumia Kharfouchi. On the nonparametric estimation of the functional regression based on censored data under strong mixing condition. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 15 (2022) no. 4, pp. 523-536. http://geodesic.mathdoc.fr/item/JSFU_2022_15_4_a10/