Distributions of (k1, k2, ⋯ , km) -runs with Multi-state Trials

Xian Zhao; Yanbo Song; Xiaoyue Wang; Zhiyue Lv

doi:10.1007/s11009-022-09948-z

Distributions of (k₁, k₂, ⋯ , k_m) -runs with Multi-state Trials

Xian Zhao
, Yanbo Song
, Xiaoyue Wang^*
, Zhiyue Lv

^*此作品的通讯作者

经济学院

Beijing Institute of Technology
Beijing Technology and Business University
AVIC China Aero-poly Technology Establishment

科研成果: 期刊稿件 › 文章 › 同行评审

6 引用（Scopus）

摘要

In this paper, six new (k₁, k₂, ⋯ , k_m) -runs with multi-state trials are proposed creatively, which can satisfy the practical needs in many fields. The exact distributions of proposed runs are obtained by applying finite Markov chain imbedding approach. This paper not only studies the case of independent identical distribution (i.i.d.) multi-state trials, but also independent non-identical distribution (non-i.i.d.) multi-state trials. Numerical examples have served the purpose to illustrate the effectiveness of the proposed approach. This study is of reference value and application significance for similar runs.

源语言	英语
页（从-至）	2689-2702
页数	14
期刊	Methodology and Computing in Applied Probability
卷	24
期	4
DOI	https://doi.org/10.1007/s11009-022-09948-z
出版状态	已出版 - 12月 2022

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10.1007/s11009-022-09948-z

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引用此

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title = "Distributions of (k1, k2, ⋯ , km) -runs with Multi-state Trials",

abstract = "In this paper, six new (k1, k2, ⋯ , km) -runs with multi-state trials are proposed creatively, which can satisfy the practical needs in many fields. The exact distributions of proposed runs are obtained by applying finite Markov chain imbedding approach. This paper not only studies the case of independent identical distribution (i.i.d.) multi-state trials, but also independent non-identical distribution (non-i.i.d.) multi-state trials. Numerical examples have served the purpose to illustrate the effectiveness of the proposed approach. This study is of reference value and application significance for similar runs.",

keywords = "(k, k, ⋯ , k) -runs, Exact distributions, Finite Markov chain imbedding approach, Multi-state trials",

author = "Xian Zhao and Yanbo Song and Xiaoyue Wang and Zhiyue Lv",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.",

year = "2022",

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doi = "10.1007/s11009-022-09948-z",

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T1 - Distributions of (k1, k2, ⋯ , km) -runs with Multi-state Trials

AU - Zhao, Xian

AU - Song, Yanbo

AU - Wang, Xiaoyue

AU - Lv, Zhiyue

PY - 2022/12

Y1 - 2022/12

N2 - In this paper, six new (k1, k2, ⋯ , km) -runs with multi-state trials are proposed creatively, which can satisfy the practical needs in many fields. The exact distributions of proposed runs are obtained by applying finite Markov chain imbedding approach. This paper not only studies the case of independent identical distribution (i.i.d.) multi-state trials, but also independent non-identical distribution (non-i.i.d.) multi-state trials. Numerical examples have served the purpose to illustrate the effectiveness of the proposed approach. This study is of reference value and application significance for similar runs.

AB - In this paper, six new (k1, k2, ⋯ , km) -runs with multi-state trials are proposed creatively, which can satisfy the practical needs in many fields. The exact distributions of proposed runs are obtained by applying finite Markov chain imbedding approach. This paper not only studies the case of independent identical distribution (i.i.d.) multi-state trials, but also independent non-identical distribution (non-i.i.d.) multi-state trials. Numerical examples have served the purpose to illustrate the effectiveness of the proposed approach. This study is of reference value and application significance for similar runs.

KW - (k, k, ⋯ , k) -runs

KW - Exact distributions

KW - Finite Markov chain imbedding approach

KW - Multi-state trials

UR - https://www.scopus.com/pages/publications/85127648406

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DO - 10.1007/s11009-022-09948-z

M3 - Article

AN - SCOPUS:85127648406

SN - 1387-5841

VL - 24

SP - 2689

EP - 2702

JO - Methodology and Computing in Applied Probability

JF - Methodology and Computing in Applied Probability

IS - 4

ER -

Distributions of (k1, k2, ⋯ , km) -runs with Multi-state Trials

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Distributions of (k₁, k₂, ⋯ , k_m) -runs with Multi-state Trials