Abstract
In this chapter we illustrate the application of non-Gaussian modeling method to the analysis of inhomogeneous discrete random processes data. Two data analytic examples, Tokyo rainfall data, a nonstationary binary process and the analysis of a simulated nonstationary Poisson process data set are shown. The latter simulation is based on research of the count of X-rays from the star Cygnus Xl data. Kashiwagi and Yanagimoto (1992), is an application of the non-Gaussian state space methodology in the analysis of nonstationary Poisson process analysis of medical data.
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© 1996 Springer Science+Business Media New York
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Kitagawa, G., Gersch, W. (1996). Modeling Inhomogeneous Discrete Processes. In: Smoothness Priors Analysis of Time Series. Lecture Notes in Statistics, vol 116. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0761-0_13
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DOI: https://doi.org/10.1007/978-1-4612-0761-0_13
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94819-5
Online ISBN: 978-1-4612-0761-0
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