Abstract
The versatility of the IG law and its interpretability as a first passage time distribution make it a strong candidate in modelling data in diverse disciplines. Several authors have employed this law as a mixing distribution to generate compound distributions that have many appealing features in fitting long-tailed data. In this chapter we examine some of its ramifications.
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© 1999 Springer Science+Business Media New York
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Seshadri, V. (1999). Compound Laws and Mixtures. In: The Inverse Gaussian Distribution. Lecture Notes in Statistics, vol 137. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1456-4_7
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DOI: https://doi.org/10.1007/978-1-4612-1456-4_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98618-0
Online ISBN: 978-1-4612-1456-4
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