Displaying similar documents to “Almost sure central limit theorems for strongly mixing and associated random variables.”

Central limit theorem for sampled sums of dependent random variables

Nadine Guillotin-Plantard, Clémentine Prieur (2010)

ESAIM: Probability and Statistics

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We prove a central limit theorem for linear triangular arrays under weak dependence conditions. Our result is then applied to dependent random variables sampled by a ℤ -valued transient random walk. This extends the results obtained by [N. Guillotin-Plantard and D. Schneider, (2003) 477–497]. An application to parametric estimation by random sampling is also provided.

A strong invariance principle for negatively associated random fields

Guang-hui Cai (2011)

Czechoslovak Mathematical Journal

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In this paper we obtain a strong invariance principle for negatively associated random fields, under the assumptions that the field has a finite ( 2 + δ ) th moment and the covariance coefficient u ( n ) exponentially decreases to 0 . The main tools are the Berkes-Morrow multi-parameter blocking technique and the Csörgő-Révész quantile transform method.