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Wang-Yun Gu

Publications and source records attributed to Wang-Yun Gu.

3 recordsLinked to original sources

On the Law of the Iterated Logarithm for m-dependent stationary random variables under sub-linear expectations

This paper explores the Law of the Iterated Logarithm (LIL) for $m$-dependent sequences under the framework of sub-linear expectations. We first extend existing LIL results to sequences of independent, non-identically distributed random variables under sub-linear expectations. This extension serves as a crucial intermediary step, facilitating the subsequent establishment of the LIL for $m$-dependent stationary sequences. On the other hand, we also establish necessary conditions for $m$-dependent sequences in sub-linear expectation spaces.

math.PR

Strong law of large numbers for $m$-dependent and stationary random variables under sub-linear expectations

The arm of this paper is to establish the strong law of large numbers (SLLN) of $m$-dependent random variables under the framework of sub-linear expectations. We establish the SLLN for a sequence of independent, but not necessarily identically distributed random variables. The study further extends the SLLN to $m$-dependent and stationary sequence of random variables with the condition $C_{\mathbb V}(|X_1|)<\infty$ which is the sufficient and necessary condition of SLLN in the case of independent and identically distributed random variables.

math.PR

Central Limit Theorem for m-dependent random variables under sub-linear expectations

M-dependence is a commonly used assumption in the study of dependent sequences. In this paper, central limit theorems for m-dependent random variables under the sub-linear expectations are established based mainly on the conditions of Zhang. They can be regarded as the extension of independent Lindeberg central limit theorem and for proving this, Rosenthal's inequality for m-dependent random variables is obtained. In particular, we extend the results in Li and establish the central limit theorem for m-dependent stationary sequence.

math.PR