• Title/Summary/Keyword: ø-mixing sequence

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ON COMPLETE CONVERGENCE OF WEIGHTED SUMS OF ø-MIXING RANDOM VARIABLES WITH APPLICATION TO MOVING AVERAGE PROCESSES

  • Baek, J.I.;Liang, H.Y.;Choi, Y.K.;Chung, H.I.
    • Journal of the Korean Statistical Society
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    • v.33 no.3
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    • pp.271-282
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    • 2004
  • We discuss complete convergence of weighted sums for arrays of ø-mixing random variables. As application, we obtain the complete convergence of moving average processes for ø-mixing random variables. The result of Baum and Katz (1965) as well as the result of Li et al. (1992) on iid case are extended to ø-mixing setting.

Validity of Blockwise Bootstrapped Empirical Process with Multivariate Stationary Sequences

  • Kim, Tae-Yoon;Shin, Ki-Dong;Song, Gyu-Moon
    • Journal of the Korean Statistical Society
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    • v.30 no.3
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    • pp.407-418
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    • 2001
  • Buhlmann(1944) established the validity of the block bootstrap proposed by Kunsch when it is applied to p-dimensional $\alpha$-mixing dependent sequence. But his result requires a rather restrictive condition on p in the sense that p is entangled with dependence structure. We address that such restriction on p(or complication of dependence structure with p) could be removed completely when the underlying dependence structure is replace by more weakly dependent structure such as ø-mixing.

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SELF-NORMALIZED WEAK LIMIT THEOREMS FOR A ø-MIXING SEQUENCE

  • Choi, Yong-Kab;Moon, Hee-Jin
    • Bulletin of the Korean Mathematical Society
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    • v.47 no.6
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    • pp.1139-1153
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    • 2010
  • Let {$X_j,\;j\geq1$} be a strictly stationary $\phi$-mixing sequence of non-degenerate random variables with $EX_1$ = 0. In this paper, we establish a self-normalized weak invariance principle and a central limit theorem for the sequence {$X_j$} under the condition that L(x) := $EX_1^2I{|X_1|{\leq}x}$ is a slowly varying function at $\infty$, without any higher moment conditions.