Existence, uniqueness and strong consistency of the maximum likelihood estimator for a model of accidents frequencies

Issa Cherif Geraldo
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引用次数: 1

Abstract

The aim of this paper is to prove the existence, uniqueness and strong consistency (i.e. almost sure convergence to the true unknown value) of the maximum likelihood estimator (MLE) of the vector parameter for a statistical model used in statistics applied to road safety. In the general case, the strong consistency of the MLE may be established by using the well-known result by Abraham Wald (in 1949) or its variants under a set of conditions. However, for the model considered in this paper, all these conditions are very difficult to verify because of the great dimension of the parameter space and the rather complex expression of the log-likelihood function. To circumvent these difficulties, we first demonstrate that the MLE exists and is unique afterwards we demonstrate the strong consistency of the MLE using the properties of the model and some theorems of mathematical analysis.
一类事故频率模型的最大似然估计的存在唯一性和强相合性
本文的目的是证明用于道路安全统计的统计模型的向量参数的极大似然估计量(MLE)的存在性、唯一性和强相合性(即几乎肯定收敛于真未知值)。一般情况下,MLE的强一致性可以通过在一组条件下使用Abraham Wald(1949)的著名结果或其变体来建立。然而,对于本文所考虑的模型来说,由于参数空间的维数很大,对数似然函数的表达式也很复杂,所以这些条件都很难得到验证。为了克服这些困难,我们首先证明了MLE的存在性和唯一性,然后利用模型的性质和数学分析的一些定理证明了MLE的强一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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