Kernel regression estimation for LTRC and associated data

This paper focuses on nonparametric regression modeling of time-series and incomplete observations. In this sense, the observations are subject to both left truncation and right censoring (LTRC) and satisfy an association dependency. Using the well-known kernel estimation method, we establish the st...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 52; no. 18; pp. 6381 - 6406
Main Authors Bey, Siham, Guessoum, Zohra, Tatachak, Abdelkader
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 17.09.2023
Taylor & Francis Ltd
Subjects
Association
Kernel regression
Kernels
optimal bandwidth
strong uniform consistency rate
truncated-censored data
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Summary:This paper focuses on nonparametric regression modeling of time-series and incomplete observations. In this sense, the observations are subject to both left truncation and right censoring (LTRC) and satisfy an association dependency. Using the well-known kernel estimation method, we establish the strong uniform consistency with a rate of the kernel estimator proposed in this paper. Simulation studies are conducted to assess the impact of both incompleteness and association dependency on the estimation.
Bibliography:ObjectType-Article-1
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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2022.2028839