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Kernel regression estimation for LTRC and associated data

Author

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  • Siham Bey
  • Zohra Guessoum
  • Abdelkader Tatachak

Abstract

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.

Suggested Citation

  • Siham Bey & Zohra Guessoum & Abdelkader Tatachak, 2023. "Kernel regression estimation for LTRC and associated data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(18), pages 6381-6406, September.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:18:p:6381-6406
    DOI: 10.1080/03610926.2022.2028839
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