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Asymptotic distribution of martingale estimatorsfor a class of epidemic models

机译:一类流行病模型mar估计的渐近分布

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This article is a contribution to the asymptotic inference on the parameters of a quite general class of stochastic models for the spread of epidemics developing in closed populations. Various epidemic models are contained within our framework, for instance, a stochastic version of the Kermack and McKendrick model and the SIS epidemic model. Each model belonging to this class, which consists in a family of discrete-time stochastic process, contains certain parameters to be estimated by means of martingale estimators. Some particular cases defined by means of Markov chains are included in our setting. The main aim of this work is to prove consistency and asymptotic normality of these estimators. Some hypothesis tests based on the main results are also shown.
机译:本文对关于封闭人群中流行病传播的相当一般的随机模型类别的参数的渐进推断做出了贡献。我们的框架中包含各种流行病模型,例如Kermack和McKendrick模型的随机版本以及SIS流行病模型。属于这一类的每个模型都包含一个离散时间随机过程族,其中包含某些参数,这些参数可以通过mar估计器进行估计。我们的环境中包括通过马尔可夫链定义的一些特殊情况。这项工作的主要目的是证明这些估计量的一致性和渐近正态性。还显示了基于主要结果的一些假设检验。

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