Estimation of variance of partial sums of an associated sequence of random variables

DOI10.1016/0304-4149(94)00065-2zbMath0817.62019OpenAlexW1986428799MaRDI QIDQ1890704

Ram Suresh, Magda Peligrad

Publication date: 23 May 1995

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4149(94)00065-2

zbMATH Keywords

Wiener processcentral limit theoreminvariance principledependent variablesvariance of partial sumsoverlapping blocksstationary sequence of associated random variablessubseries values

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Central limit and other weak theorems (60F05) Point estimation (62F10)

Related Items (17)

A nonclassical law of the iterated logarithm for functions of positively associated random variables ⋮ A self-normalized central limit theorem for a ρ-mixing stationary sequence ⋮ Nonparametric confidence intervals for location in time series data ⋮ On estimation of limiting variance of partial sums of functions of associated random variables ⋮ Predictive inference for travel time on transportation networks ⋮ Estimation of the variance for strongly mixing sequences ⋮ Estimation of the limit variance for sums under a new weak dependence condition ⋮ A self-normalized central limit theorem for \(\rho \)-mixing stationary sequences ⋮ Wilcoxon-signed rank test for associated sequences ⋮ A self-normalized invariance principle for a \(\phi\)-mixing sequence ⋮ The weak convergence for functions of negatively associated random variables ⋮ Empirical Euclidean likelihood for general estimating equations under association dependence ⋮ Normal approximation for quasi-associated random fields ⋮ Kaplan-Meier estimator under association ⋮ Empirical likelihood ratio confidence interval for positively associated series ⋮ Self-normalized central limit theorem and estimation of variance of partial sums for negative dependent random variables ⋮ Maximal inequalities for demimartingales and a strong law of large numbers

Cites Work

• Unnamed Item
• The use of subseries values for estimating the variance of a general statistic from a stationary sequence
• An invariance principle for certain dependent sequences
• Self-normalized central limit theorem for sums of weakly dependent random variables
• Estimation of the variance of partial sums for \(\rho\)-mixing random variables
• The jackknife and the bootstrap for general stationary observations

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