<?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 Ramos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sample Partitioning Estimation for Ergodic Diffusions: Application to Ornstein-Uhlenbeck Diffusion</style></title><secondary-title><style face="normal" font="default" size="100%">Discussiones Mathematicae Probability and Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.discuss.wmie.uz.zgora.pl/php/discuss3.php?ip=&amp;url=plik&amp;nIdA=21457&amp;sTyp=HTML&amp;nIdSesji=-1</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">117-122</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;When a diffusion is ergodic its transition density converges to its invariant density, see Durrett (1998).   This convergence enabled us to introduce a sample partitioning technique that gives in each sub-sample, maximum likelihood estimators. The averages of these being a natural choice as estimators.  To compare our estimators with the optimal we obtained from martingale estimating functions, see Sorensen (1998), we used the Ornstein-Uhlenbeck process for which exact simulations can be carried out.&lt;/p&gt;
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