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Incomplete-data Fisher scoring method with steplength adjustment
Statistics and Computing ( IF 2.2 ) Pub Date : 2020-02-05 , DOI: 10.1007/s11222-020-09923-z
Keiji Takai

An incomplete-data Fisher scoring method is proposed for parameter estimation in models where data are missing and in latent-variable models that can be formulated as a missing data problem. The convergence properties of the proposed method and an accelerated variant of this method are provided. The main features of this method are its ability to accelerate the rate of convergence by adjusting the steplength, to provide a second derivative of the observed-data log-likelihood function using only the functions used in the proposed method, and the ability to avoid having to explicitly solve the first derivative of the object function. Four examples are presented to demonstrate how the proposed method converges compared with the EM algorithm and its variants. The computing time is also compared.

中文翻译:

带步长调整的不完整数据费舍尔评分方法

提出了一种不完整数据的Fisher评分方法,用于数据缺失的模型中的参数估计以及可以表述为缺失数据问题的潜在变量模型。提供了所提出方法的收敛性和该方法的加速变体。该方法的主要特征是它能够通过调整步长来加快收敛速度​​,仅使用所提出方法中使用的函数来提供观测数据对数似然函数的二阶导数,以及避免具有明确解决对象函数的一阶导数。给出了四个示例,以证明与电磁算法及其变体相比,所提出的方法如何收敛。还比较了计算时间。
更新日期:2020-02-05
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