随机偏微分方程的PROHOROV度量框架和聚合数据逆问题。

Communications in applied analysis Pub Date : 2018-01-01 Epub Date: 2018-06-19
H T Banks, K B Flores, I G Rosen, E M Rutter, Melike Sirlanci, W Clayton Thompson
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引用次数: 0

摘要

我们考虑在只有总体(总体水平)数据可用的问题中参数的概率测度的非参数估计。我们总结了几十年来发展起来的现有估计问题的计算方法[24,5,12,28,16]。给出了一些理论结果,证明了随机偏微分方程测度估计问题的一般最小二乘估计和估计的存在性和相合性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs.

THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs.

THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs.

THE PROHOROV METRIC FRAMEWORK AND AGGREGATE DATA INVERSE PROBLEMS FOR RANDOM PDEs.

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 [24, 5, 12, 28, 16]. 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.

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