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Conditions for equivalence between Mallows distance and convergence to stable laws

Acta Mathematica Hungarica
Volume/Issue:
Volume 134: Issue 1-2
DOI:
https://doi.org/10.1007/s10474-011-0101-7
Authors: Chang C. Y. Dorea and Debora B. Ferreira

] Hall , P. , Heyde , C. C. 1980 Martingale Limit Theory and Its Applications Academic Press New York . [6] Johnson , O. , Samworth , R. 2005 Central limit theorem and convergence to

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Some multivariate inequalities with applications

Periodica Mathematica Hungarica
Volume/Issue:
Volume 51: Issue 2
DOI:
https://doi.org/10.1007/s10998-005-0029-1
Authors: Sana Louhichi and Sofyen Louhichi

Summary  

Let \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} ${\cal {X}}_{n} =(X_1,\ldots,X_n)$ \end{document} be a random vector. Suppose that the random variables \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $(X_i)_{1\leq i\leq n}$ \end{document} are stationary and fulfill a suitable dependence criterion. Let \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $f$ \end{document} be a real valued function defined on \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $\mathbbm{R}^n$ \end{document} having some regular properties. Let \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} ${\cal {Y}}_{n}$ \end{document} be a random vector, independent of \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} ${\cal {X}}_{n}$ \end{document}, having independent and identically distributed components. We control \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $\left|\mathbbm{E}(f({\cal {X}}_{n}))-\mathbbm{E} (f({\cal {Y}}_{n}))\right|$ \end{document}. Suitable choices of the function \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $f$ \end{document} yield, under minimal conditions, to rates of convergence in the central limit theorem, to some moment inequalities or to bounds useful for Poisson approximation. The proofs are derived from multivariate extensions of Taylor's formula and of the Lindeberg decomposition. In the univariate case and in the mixing setting the method is due to Rio (1995).

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A note on sub-independent random variables and a class of bivariate mixtures

Studia Scientiarum Mathematicarum Hungarica
Volume/Issue:
Volume 49: Issue 1
DOI:
https://doi.org/10.1556/sscmath.2011.1183
Authors: G. Hamedani, Hans Volkmer, and J. Behboodian

central limit theorem, Arch. Math. , 43 (1984), no. 3, pp. 258–264. MR 0766433 ( 86a :60029) Walter G. G. A fixed point theorem and its application to the central limit theorem

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A characterization of signed discrete infinitely divisible distributions

Studia Scientiarum Mathematicarum Hungarica
Volume/Issue:
Volume 54: Issue 4
DOI:
https://doi.org/10.1556/012.2017.54.4.1377
Authors: Huiming Zhang, Bo Li, and G. Jay Kerns

Mathematicum , 10 ( 6 ) ( 1998 ), 687 – 698 . [2] Baez-Duarte , L. , Central limit theorem for complex measures , Journal of

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On the strong law of large numbers for ϕ-mixing and ρ-mixing random variables

Acta Mathematica Hungarica
Volume/Issue:
Volume 132: Issue 1-2
DOI:
https://doi.org/10.1007/s10474-011-0089-z
Author: Anna Kuczmaszewska

. [10] Peligrad , M. 1987 On the central limit theorem for ρ -mixing sequences of random variables Ann. Probab. 15 1387 – 1394 10.1214/aop

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Small ball estimates for Brownian motion under a weighted sup-norm

Studia Scientiarum Mathematicarum Hungarica
Volume/Issue:
Volume 36: Issue 1-2
DOI:
https://doi.org/10.1556/sscmath.36.2000.1-2.17
Authors: Ph. Berthet and Z. Shi

. 8 361 386 ACOSTA, A. DE, Small deviations in the functional central limit theorem with applications to functional laws of the iterated logarithm

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Bounds on moments of symmetric statistics

Studia Scientiarum Mathematicarum Hungarica
Volume/Issue:
Volume 39: Issue 3-4
DOI:
https://doi.org/10.1556/sscmath.39.2002.3-4.1
Authors: R. Ibragimov and Sh. Sharakhmetov

. 22 1044 1077 EGOROV, V. A., On a central limit theorem with random normalization, Rings and modules. Limit theorems of probability theory , No. 1

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A rejoinder on energy versus impact indicators

Scientometrics
Volume/Issue:
Volume 90: Issue 2
DOI:
https://doi.org/10.1007/s11192-011-0502-y
Authors: Loet Leydesdorff and Tobias Opthof

and uses averages on the assumption of the Central Limit Theorem (Glänzel 2010 ). However, citation distributions are extremely skewed (Seglen 1992 , 1997 ; cf. Leydesdorff 2008 ) and central tendency statistics give misleading results. Using

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Teachers and prospective teachers’ conceptions about averages

Journal of Adult Learning, Knowledge and Innovation
Volume/Issue:
Accepted Manuscript / Online First
DOI:
https://doi.org/10.1556/2059.03.2019.02
Authors: Karin Landtblom and Lovisa Sumpter

. , & Clark , J. ( 2003 ). Successful students’ conceptions of mean, standard deviation, and the Central Limit Theorem (Unpublished paper). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.446.6077&rep=rep1&type

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Universality of performance indicators based on citation and reference counts

Scientometrics
Volume/Issue:
Volume 93: Issue 2
DOI:
https://doi.org/10.1007/s11192-012-0694-9
Authors: T. S. Evans, B. S. Kaube, and N. Hopkins

that its variance changes with time. For the case β = 0 the central limit theorem tells us that the variance should scale as σ 2 ∼ t −1 where t denotes the number of elapsed time steps. This would be manifested in a systematic temporal variation in

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