Weak convergence for weighted sums of a class of random variables with related statistical applications

Zheng, S.; Zhang, Fei; Wang, Chunhua; Wang, Xuejun

doi:10.1080/02331888.2023.2227984

Statistics

2023

DOI: 10.1080/02331888.2023.2227984

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Weak convergence for weighted sums of a class of random variables with related statistical applications

S. Zheng

Fei Zhang

Chunhua Wang

et al.

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2024

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On complete convergence for weighted sums of m -widely acceptable random variables under sub-linear expectations and its statistical applications

Deng,

Ding,

et al. 2024

Stochastic Models

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In this paper, by means of Rosenthal-type inequality, we study the complete convergence and strong law of large numbers for weighted sums of m-widely accpetable random variables under sub-linear expectations, which extend some existing results in classical probability space. As applications, we investigate the complete consistency and strong consistency of weighted estimators in nonparametric regression models under sub-linear expectations. In addition, we provide some simulation studies to verify the finite sample performance of the theoretical results.

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On complete convergence for weighted sums of m -widely acceptable random variables under sub-linear expectations and its statistical applications

Deng,

Ding,

et al. 2024

Stochastic Models

View full text Add to dashboard Cite

show abstract

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Weak convergence for weighted sums of a class of random variables with related statistical applications

Cited by 1 publication

References 31 publications

On complete convergence for weighted sums of m -widely acceptable random variables under sub-linear expectations and its statistical applications

On complete convergence for weighted sums of m -widely acceptable random variables under sub-linear expectations and its statistical applications

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