@article {doi:10.1080/03610918.2013.765471,
	title = {Sample Partitioning Estimation for Ergodic Diffusions},
	journal = {Communications in Statistics - Simulation and Computation},
	volume = {44},
	number = {1},
	year = {2015},
	note = {n/a},
	pages = {105-117},
	abstract = {<p>In this article, we present a new technique to obtain estimators for parameters of ergodic processes. When a diffusion is ergodic its transition density converges to the invariant density Durett (1996). This convergence enabled us to introduce a sample partitioning technique that gives, in each subsample, observations that can be treated as independent and identically distributed. Within this framework, is possible the construction of estimators like maximum likelihood estimators or others.</p>
},
	doi = {10.1080/03610918.2013.765471},
	url = {http://dx.doi.org/10.1080/03610918.2013.765471},
	author = {Lu{\'\i}s P. Ramos and Mota, Pedro and Jo{\~a}o T. Mexia}
}
