Circular autocorrelation of stationary circular Markov processes

Toshihiro Abe (), Hiroaki Ogata (), Takayuki Shiohama () and Hiroyuki Taniai ()
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Toshihiro Abe: Nanzan University
Hiroaki Ogata: Tokyo Metropolitan University
Takayuki Shiohama: Tokyo University of Science
Hiroyuki Taniai: Waseda University

Statistical Inference for Stochastic Processes, 2017, vol. 20, issue 3, No 2, 275-290

Abstract: Abstract The stationary Markov process is considered and its circular autocorrelation function is investigated. More specifically, the transition density of the stationary Markov circular process is defined by two circular distributions, and we elucidate the structure of the circular autocorrelation when one of these distributions is uniform and the other is arbitrary. The asymptotic properties of the natural estimator of the circular autocorrelation function are derived. Furthermore, we consider the bivariate process of trigonometric functions and provide the explicit form of its spectral density matrix. The validity of the model was assessed by applying it to a series of wind direction data.

Keywords: Circular statistics; Time series models; Toroidal data; Wind direction; Wrapped cauchy distribution (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11203-016-9154-0

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