Numerical methods for solving a class of matrix equations arising from inference for ranked set sampling on imperfect ranking

Qiang Niu*, Binrui Shen, Yenan Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we investigate some numerical methods for a class of matrix equations with constraints arising from inference for ranked set sampling on imperfect ranking. Based on the structure of the matrix equation, two classes of numerical methods are studied to solve the problem. The first idea is to treat the problem as a simplified Riccati equation, then a Schur method and a square-root method are derived. The second idea is entirely novel, which is based on an extended Krylov subspace originated from the doubly stochastic property of the related matrices. The performance and efficiency of all the numerical solvers are verified by numerical examples.

Original languageEnglish
Article number116519
JournalJournal of Computational and Applied Mathematics
Volume463
DOIs
Publication statusPublished - 1 Aug 2025

Keywords

  • Doubly stochastic matrix
  • Krylov subspace
  • Matrix equation
  • Riccati equation
  • Schur's method

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