Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239289 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 9 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-13
Publisher: 
MDPI, Basel
Abstract: 
In this paper, we estimate the Shannon entropy S(f)=-E[log(f(x))] of a one-sided linear process with probability density function f(x). We employ the integral estimator Sn(f), which utilizes the standard kernel density estimator fn(x) of f(x). We show that Sn(f) converges to S(f) almost surely and in ¡2 under reasonable conditions.
Subjects: 
kernel entropy estimation
linear process
Shannon entropy
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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