关于减少阶段结构化发展模型参数估计中的偏差

IF 0.8 Q3 STATISTICS & PROBABILITY
Hoa Pham, Huong T. T. Pham, Kai Siong Yow
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引用次数: 0

摘要

在 Metropolis-Hastings(MH)算法中采用确定性变换的贝叶斯方法被用来估计这些阶段结构模型的参数。然而,后期阶段的偏差是这种方法的局限性,尤其是对具有三个以上阶段的模型的估计精度。本文的主要目的是减少参数估计中的这些偏差。具体而言,我们将不重要的前一阶段或可忽略的后一阶段结合起来,以估计所需阶段的参数,同时对非危险率模型采用基于确定性变换的调整 MH 算法。结果表明,所提出的方法可以减少后期阶段对阶段结构模型估计的偏差,案例研究的结果可视为大流行病预防的宝贵延续。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On bias reduction in parametric estimation in stage structured development models
Multi-stage models for cohort data are popular statistical models in several fields such as disease progressions, biological development of plants and animals, and laboratory studies of life cycle development. A Bayesian approach on adopting deterministic transformations in the Metropolis–Hastings (MH) algorithm was used to estimate parameters for these stage structured models. However, the biases in later stages are limitations of this methodology, especially the accuracy of estimates for the models having more than three stages. The main aim of this paper is to reduce these biases in parameter estimation. In particular, we conjoin insignificant previous stages or negligible later stages to estimate parameters of a desired stage, while an adjusted MH algorithm based on deterministic transformations is applied for the non-hazard rate models. This means that current stage parameters are estimated separately from the information of its later stages. This proposed method is validated in simulation studies and applied for a case study of the incubation period of COVID-19. The results show that the proposed methods could reduce the biases in later stages for estimates in stage structured models, and the results of the case study can be regarded as a valuable continuation of pandemic prevention.
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来源期刊
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications STATISTICS & PROBABILITY-
CiteScore
1.20
自引率
22.20%
发文量
31
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