A new proof of strong consistency of kernel estimation of density function and mode under random censorship

Ali Gannoun and Jérôme Saracco

Statistics & Probability Letters, 2002, vol. 59, issue 1, 61-66

Abstract: 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.

Keywords: Censored; data; Kaplan-Meier; estimator; Kernel; density; estimation; Mode; estimation; Strong; consistency (search for similar items in EconPapers)
Date: 2002
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