Markov-modulated dependencies for a multiplicativeLindley-type recursion

In this paper, we study the stationary behaviour of a Markov-modulated multiplicative Lindley-type recursion W n+1 = [V n W n + Y n (V n )] + . Given the state of the background Markov chain, we consider two cases: a) V n equals either 1, or a ∈ (0, 1), or it is negative with certain probabilities, and Y n (V n ) := Y n = S n - A n+1 , where both A n and S n have a rational Laplace-Stieltjes transform (LST). b) V n equals 1 or -1 according to certain probabilities, and Y n (V n ) follows a more general scheme, dependent on V n . In both cases, we derive the LST of the stationary transform vector of W n n ∈ ℕ 0 . In the second case, we also provide a recursive approach to obtain the steady-state moments and provide an asymptotic result. A simple numerical example illustrates the theoretical findings.

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Publication Details

Journal
ACM SIGMETRICS Performance Evaluation Review
Published
2026-03-31
DOI
https://doi.org/10.1145/3797823.3797847
Primary Topic
Probability and Risk Models
Type
article
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article

Markov-modulated dependencies for a multiplicativeLindley-type recursion

Ioannis Dimitriou
ACM SIGMETRICS Performance Evaluation Review
Probability and Risk Models
article

Markov-modulated dependencies for a multiplicativeLindley-type recursion

Ioannis Dimitriou
article en

Abstract

In this paper, we study the stationary behaviour of a Markov-modulated multiplicative Lindley-type recursion W n+1 = [V n W n + Y n (V n )] + . Given the state of the background Markov chain, we consider two cases: a) V n equals either 1, or a ∈ (0, 1), or it is negative with certain probabilities, and Y n (V n ) := Y n = S n - A n+1 , where both A n and S n have a rational Laplace-Stieltjes transform (LST). b) V n equals 1 or -1 according to certain probabilities, and Y n (V n ) follows a more general scheme, dependent on V n . In both cases, we derive the LST of the stationary transform vector of W n n ∈ ℕ 0 . In the second case, we also provide a recursive approach to obtain the steady-state moments and provide an asymptotic result. A simple numerical example illustrates the theoretical findings.

ACM SIGMETRICS Performance Evaluation ReviewVol. 53(4)
University of Ioannina (GR)
Openalex Percentile: Top 5%
Probability and Risk Models
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