The Strong Law of Large Numbers for Negatively Dependent Generalized Gaussian Random Variables
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Publication:4826123
DOI10.1081/SAP-120037623zbMath1056.60024OpenAlexW1969680153MaRDI QIDQ4826123
H. A. Azarnoosh, Mohammad Amini-Dehak, Abolghassem Bozorgnia
Publication date: 11 November 2004
Published in: Stochastic Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1081/sap-120037623
strong law of large numbersmartingaleweighted sumsnegatively dependent generalized Gaussian random variables
Related Items (12)
Convergence of series of strongly integrable random variables and applications ⋮ Some strong limit theorems of weighted sums for negatively dependent generalized Gaussian random variables ⋮ On the strong convergence rate for weighted sums of arrays of rowwise negatively orthant dependent random variables ⋮ Complete convergence for arrays of rowwise negatively orthant dependent random variables ⋮ Complete convergence for weighted sums of negatively dependent random variables ⋮ Convergence of series of dependent \(\varphi \)-sub-Gaussian random variables ⋮ The strong consistency of the estimator of fixed-design regression model under negatively dependent sequences ⋮ Some exponential inequalities for negatively orthant dependent random variables ⋮ Weak laws of large numbers for arrays of dependent random variables ⋮ On the rate of convergence in the strong law of large numbers for negatively orthant-dependent random variables ⋮ Complete moment convergence for negatively orthant dependent random variables and its applications in statistical models ⋮ Complete convergence and complete moment convergence for arrays of rowwise negatively dependent random variables under sub-linear expectations
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