<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luís P. Ramos</style></author><author><style face="normal" font="default" size="100%">Mota, Pedro</style></author><author><style face="normal" font="default" size="100%">João T. Mexia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sample Partitioning Estimation for Ergodic Diffusions</style></title><secondary-title><style face="normal" font="default" size="100%">Communications in Statistics - Simulation and Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1080/03610918.2013.765471</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">105-117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record></records></xml>