State-space modelling of time series sampled from continuous processes with pulses

Abstract

SUMMARY A non-Gaussian time series model useful for describing the dynamics of endocrinological data is introduced by using a state-space representation. This model is applicable even when the data are sparsely sampled in comparison with time lengths of pulses. Likelihood-based methods of inference for the model are developed. By fitting the present model to partially observed hormonal time series data, we can estimate the locations and heights of the peaks of the pulses as well as their onset times. Models with different shapes of pulses are considered for a comparison of the goodness of fit.