A Tweedie Markov process and its application in fisheries stock assessment

IF 1.3 4区 数学 Q3 STATISTICS & PROBABILITY Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-07-20 DOI:10.1093/jrsssc/qlad064
Nan Zheng, Y. Lim, N. Cadigan
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Abstract

The Tweedie distribution is a useful tool to model zero-inflated non-negative continuous data. However, the Tweedie dispersion relationship (DR) is not general enough to cover some important forms such as quadratic dispersion, and an easy and fast-to-implement Tweedie AR(1) model (first-order autoregressive model) needs to be developed for spatio-temporal modelling. In this research we extend the Tweedie distribution to accommodate flexible DRs, and propose a Tweedie Markov process (TMP) with the AR(1) autocorrelation structure. This TMP is simple to implement and requires only the Tweedie probability density function. Simulation studies and real data analysis are conducted to validate our new approach.
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Tweedie Markov过程及其在渔业资源评价中的应用
Tweedie分布是对零膨胀非负连续数据建模的一个有用工具。然而,Tweedie色散关系(DR)不够通用,无法涵盖二次色散等一些重要形式,需要开发一种易于实现的Tweedie AR(1)模型(一阶自回归模型)进行时空建模。在本研究中,我们扩展了Tweedie分布以适应灵活的dr,并提出了一个具有AR(1)自相关结构的Tweedie马尔可夫过程(TMP)。这个TMP很容易实现,只需要Tweedie概率密度函数。仿真研究和实际数据分析验证了我们的新方法。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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