We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where...

This thesis consists of two parts. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis consists of two chapters. One chapter is concerned with the development of efficient estimators of the marginal distribution functions...