computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

We consider a large class of exponential random graph models and prove the existence of a region of parameter space corresponding to emergent multipartite structure, separated by a phase transition from a region of disordered graphs. An essential feature is the formalism of graph limits as developed by Lovász et al for dense random graphs.

This paper presents the theoretical development of new threshold autoregressive models based on trended time series. The theoretical arguments underlying the models are outlined and a nonlinear economic model is used to derive the speci cation of the empirical econometric models. Estimation and testing issues are considered and analysed. Additionally we apply the models to the empirical investi...

This paper describes a new approach to time series modeling that combines subject-matter knowledge of the system dynamics with statistical techniques in time series analysis and regression. Applications to American option pricing and the Canadian lynx data are given to illustrate this approach.

The climatic importance of the microphysical and radiative properties of clouds, particularly cloud droplet effective radii (re), optical depths (τ), and albedos are widely recognized. For reliable application of satellite datasets in cloud process and single column models, it is important to have a reasonable estimate of the errors in the derived cloud properties. When properly used, ground-ba...

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependen...