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Smoothing signals for semimartingales

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  • Thavaneswaran, A.

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.

Suggested Citation

  • Thavaneswaran, A., 1988. "Smoothing signals for semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 28(1), pages 81-89, April.
    • Handle: RePEc:eee:spapps:v:28:y:1988:i:1:p:81-89
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    Cited by:

    1. A. Thavaneswaran & Jagbir Singh, 1993. "A note on smoothed estimating functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(4), pages 721-729, December.

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