时空自回归希尔伯特模型及其在风速中的应用

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Australian & New Zealand Journal of Statistics Pub Date : 2025-08-21 DOI:10.1111/anzs.70020

Maryam Hashemi, Atefeh Zamani, Roya Nasirzadeh

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引用次数: 0

摘要

提出了一种基于时空自回归希尔伯特模型的时空泛函数据分析新方法。该模型可以捕捉功能数据随时间和空间的动态和空间依赖性。给出了该模型存在唯一性的充分条件，并建立了该模型的渐近性质，如强大数定律和中心极限定理。我们还开发了一个模型参数的一致性估计器，并通过风速数据的仿真和实例对其性能进行了评价。

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Space-Time Autoregressive Hilbertian Models and Their Application for Wind Speed

We propose a novel approach for space-time functional data analysis based on a space-time autoregressive Hilbertian model. This model can capture the dynamic and spatial dependence of functional data over time and space. We provide sufficient conditions for the existence and uniqueness of the model and establish its asymptotic properties, such as the strong law of large numbers and the central limit theorem. We also develop a consistent estimator for the model parameters and evaluate its performance through simulations and a real example on wind speed data.

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来源期刊

Australian & New Zealand Journal of Statistics 数学-统计学与概率论

CiteScore

1.30

自引率

9.10%

发文量

审稿时长

>12 weeks

期刊介绍： The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.