<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H.T. BANKS</style></author><author><style face="normal" font="default" size="100%">K.B. FLORES</style></author><author><style face="normal" font="default" size="100%">I.G. ROSEN</style></author><author><style face="normal" font="default" size="100%">E.M. RUTTER</style></author><author><style face="normal" font="default" size="100%">MELIKE SIRLANCI</style></author><author><style face="normal" font="default" size="100%">W. CLAYTON THOMPSON</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs</style></title><secondary-title><style face="normal" font="default" size="100%">Communications in Applied Analysis</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">34A55</style></keyword><keyword><style  face="normal" font="default" size="100%">46S50</style></keyword><keyword><style  face="normal" font="default" size="100%">62G07</style></keyword><keyword><style  face="normal" font="default" size="100%">93E24</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://acadsol.eu/en/articles/22/3/6.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">32</style></pages><isbn><style face="normal" font="default" size="100%">1083-2564</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We consider nonparametric estimation of probability measures for parameters in problems where only aggregate (population level) data are available. We summarize an existing computational method for the estimation problem which has been developed over the past several decades \cite{BaBi,BBPP,BF,BHT2014,BKTReview}. &amp;nbsp;Theoretical results are presented which establish the existence and consistency of very general (ordinary, generalized and other) least squares estimates and estimators for the measure estimation problem with specific application to random PDEs.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">scientific: mathematics</style></work-type><section><style face="normal" font="default" size="100%">415</style></section></record></records></xml>