The Subdominant Eigenvalue of Möbius Monotone Transition Probability Matrix

We establish a Perron–Frobenius-type theorem for the subdominant eigenvalue of Möbius monotone transition matrices defined on partially ordered state spaces. This result extends the classical work of Keilson and Kester, where they considered stochastically monotone transition matrices in a totally ordered setting. Furthermore, we show that this subdominant eigenvalue is the geometric ergodicity rate.

Authors

Institutions

Publication Details

Journal
Axioms
Published
2025-06-25
DOI
https://doi.org/10.3390/axioms14070493
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Subdominant Eigenvalue of Möbius Monotone Transition Probability Matrix

Pei-Sen Li, Pan Zhao
Axioms
Random Matrices and Applications
article

The Subdominant Eigenvalue of Möbius Monotone Transition Probability Matrix

Pei-Sen Li, Pan Zhao
article en

Abstract

We establish a Perron–Frobenius-type theorem for the subdominant eigenvalue of Möbius monotone transition matrices defined on partially ordered state spaces. This result extends the classical work of Keilson and Kester, where they considered stochastically monotone transition matrices in a totally ordered setting. Furthermore, we show that this subdominant eigenvalue is the geometric ergodicity rate.

AxiomsVol. 14(7)
Beijing Union University (CN), Beijing Institute of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 85%
Random Matrices and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.