A new proof of strong consistency of kernel estimation of density function and mode under random censorship
In this paper, we establish a new proof of uniform consistency of kernel estimator of density function when we observe a random right censored model. This proof uses an exponential inequality established by Wang (2000). As a consequence, we obtain the almost sure convergence of the kernel estimator of the mode.
Year of publication: |
2002
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Authors: | Gannoun, Ali ; Saracco, Jérôme |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 59.2002, 1, p. 61-66
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Publisher: |
Elsevier |
Keywords: | Censored data Kaplan-Meier estimator Kernel density estimation Mode estimation Strong consistency |
Saved in:
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