Shannon Entropy Estimation for Linear Processes

Timothy Fortune and Hailin Sang
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Timothy Fortune: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
Hailin Sang: Department of Mathematics, University of Mississippi, University, MS 38677, USA

JRFM, 2020, vol. 13, issue 9, 1-13

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 S n ( f ) , which utilizes the standard kernel density estimator f n ( x ) of f ( x ) . We show that S n ( f ) converges to S ( f ) almost surely and in ? 2 under reasonable conditions.

Keywords: linear process; kernel entropy estimation; Shannon entropy (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
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