Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...

We describe a simulation-based method of inference for parametric measurement error models in which the measurement error variance is known or at least well estimated. The method entails adding additional measurement error in known increments to the data, computing estimates from the contaminated data, establishing a trend between these estimates and the variance of the added errors, and extrap...

The shape of anatomic objects often depends in complex ways on the shapes and locations of neighboring objects. Shape parameter networks provide an approach for representing shape dependencies and producing multi-object models that share consistent boundary definitions. This paper provides an overview of the modeling framework provided by shape parameter networks, and demonstrates their use thr...