Statistical Inference for Stochastic Processes
An International Journal devoted to Time Series Analysis and the Statistics of Continuous Time Processes and Dynamical Systems
Statistical Inference for Stochastic Processes is an international journal publishing articles on parametric and nonparametric inference for discrete- and continuous-time stochastic processes, and their applications to biology, chemistry, physics, finance, economics, and other sciences.
Self-weighted generalized empirical likelihood methods for hypothesis testing in infinite variance ARMA models
Fumiya Akashi (April 2017)
To view the rest of this content please follow the download PDF link above.