Self-normalized central limit theorem and estimation of variance of partial sums for negative dependent random variables

DOI10.1007/S11766-002-0012-ZzbMath1002.62025OpenAlexW2075928793MaRDI QIDQ1847631

Strongway Shi, Zhang, Lixin

Publication date: 13 January 2003

Published in: Applied Mathematics. Series B (English Edition) (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11766-002-0012-z

zbMATH Keywords

consistencynegative dependencevariance of partial sums

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Central limit and other weak theorems (60F05) Strong limit theorems (60F15)

Related Items (2)

A self-normalized central limit theorem for a ρ-mixing stationary sequence ⋮ A self-normalized invariance principle for a \(\phi\)-mixing sequence

Cites Work

• An invariance principle for certain dependent sequences
• A note on the almost sure convergence of sums of negatively dependent random variables
• Estimation of the variance of partial sums for \(\rho\)-mixing random variables
• Moment inequalities and weak convergence for negatively associated sequences
• An invariance principle for negatively associated random variables
• The law of iterated logarithm for negatively associated random variables
• Negative association of random variables, with applications
• Estimation of variance of partial sums of an associated sequence of random variables
• A functional central limit theorem for asymptotically negatively dependent random fields
• The weak convergence for functions of negatively associated random variables

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