Work

A Mixture Transition Distribution Modeling for Higher‐Order Circular Markov Processes

Year: 2024

Type: article

Abstract: ABSTRACT This study considers the stationary higher‐order Markov process for circular data by employing the mixture transition distribution modeling. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models. The structures of the circular autocorrelation function, together with the circular partial autocorrelation function, are investigated. They are found to be similar to those of the autocorrelation and partial autocorrelation functions of the real‐valued autoregressive process when the underlying binding density has zero sine moments. The validity of the model is assessed by applying it to some Monte Carlo simulations and real directional data.

Source: Journal of Time Series Analysis

DOI: 10.1111/jtsa.12803

Authors: Hiroaki Ogata, Takayuki Shiohama

Institutions: Tokyo Metropolitan University, Nanzan University

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,340

Funders: Nanzan University, Japan Society for the Promotion of Science