Parameter estimation for binary time series using partial likelihood

Parameter estimation using chaotic time series

A B S T R A C T We show how the response of a chaotic model to temporally varying external forcing can be efficiently tuned via parameter estimation using time series data, extending previous work in which an unforced climatologically steady state was used as the tuning target. Although directly fitting a long trajectory of a chaotic deterministic model to a time series of data is generally not...

Parameter Estimation for Artfima Time Series

The ARTFIMA model applies a tempered fractional difference to the standard ARMA time series. This paper develops parameter estimation methods for the ARTFIMA model, and demonstrates a new R package. Several examples illustrate the utility of the method.

Parameter Estimation for Periodically Stationary Time Series

The innovations algorithm can be used to obtain parameter estimates for periodically stationary time series models. In this paper we compute the asymptotic distribution for these estimates in the case where the innovations have a finite fourth moment. These asymptotic results are useful to determine which model parameters are significant. In the process, we also develop asymptotics for the Yule...

Parameter Estimation for Tempered Fractional Time Series

Parameter estimation using Volterra series

A polynomial approximation to the likelihood function allows for marginalised estimates of model parameters to be obtained in the form of a Volterra series. The series can be applied directly to the observed data vector in an iterative fashion, to converge upon a set of parameter MAP estimates with low computational cost. A sample application towards OCR is used as an illustration.