Smoothing signals for semimartingales

A. Thavaneswaran

Stochastic Processes and their Applications, 1988, vol. 28, issue 1, 81-89

Abstract: The kernel function and convolution-smoothing methods developed to estimate a probability density function and distribution are essentially a way of smoothing the empirical distribution function. This paper shows now one can generalize these methods to estimate signals for a semimartingale model. A convolution-smoothed estimate is used to obtain an absolutely continuous estimate for an absolutely continuous signal of a semimartingale model. This provides a method of obtaining a convolution-smoothed estimate of the cumulative hazard function in the censored case, an open problem proposed by Mack (Bulletin of Informatics and Cybernetics 21 (1984) 29-35). Asymptotic properties of the convolution-smoothed estimate are discussed in some detail.

Keywords: convolution-smoothing; kernel; functions; semimartingales; signals; smoothing (search for similar items in EconPapers)
Date: 1988
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