Recently there has been a growing interest in the problems of inference for stochastic processes when the underlying distribution is not specified in terms of a parametric family. Godambe's (1985) approach is here employed to obtain estimates for random signals for a continuous semimartingale...

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...