Models for circular data from time series spectra
Models for circular data from time series spectra
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时间序列谱循环数据模型
DOI：
10.1111/jtsa.12549
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发表时间：
2020
影响因子：
0.9
通讯作者：
Hiroaki Ogata & Arthur Pewsey
中科院分区：
文献类型：
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作者：
Masanobu Taniguchi;Shogo Kato;Hiroaki Ogata & Arthur Pewsey
圆形数据是以单位圆及其环形延伸为自然支撑的数据。已经提出了许多可用于为这些数据生成模型的构造。我们提出了一个新的，非常一般的，基于复值平稳过程谱归一化的方法。作为新结构应用的例证，我们研究了从自回归滑动平均模型的谱中获得的单变量循环数据的模型，并将它们与文献中的现有模型联系起来。我们还提出并研究了由平稳随机过程的高阶谱得到的多元循环模型，该模型是由自回归滑动平均响应函数的线性滤波产生的。还介绍了圆周上马尔可夫过程的一族新的分布族。给出了圆上相依观测值的渐近最优推断结果，为圆模型的推断提供了一种新的范式。通过对风向资料的分析，说明了一类新的谱生成模型的应用。
Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex‐valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high‐order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra‐generated models is illustrated in an analysis of wind direction data.