Continuation of Convergence for Distribution Functions of Sums of Weakly Dependent Random Variables

Mathematische Nachrichten ◽

10.1002/mana.19911510120 ◽

1991 ◽

Vol 151 (1) ◽

pp. 311-314 ◽

Cited By ~ 1

Author(s):

H. Herrmann

Keyword(s):

Random Variables ◽

Distribution Functions ◽

Dependent Random Variables ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Almost sure invariance principles for empirical distribution functions of weakly dependent random variables

Empirical Distributions and Processes - Lecture Notes in Mathematics ◽

10.1007/bfb0096881 ◽

1976 ◽

pp. 83-105

Author(s):

Walter Philipp

Keyword(s):

Empirical Distribution ◽

Random Variables ◽

Distribution Functions ◽

Dependent Random Variables ◽

Invariance Principles ◽

Weakly Dependent Random Variables ◽

Weakly Dependent ◽

Empirical Distribution Functions

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A Functional Law of the Iterated Logarithm for Empirical Distribution Functions of Weakly Dependent Random Variables

The Annals of Probability ◽

10.1214/aop/1176995795 ◽

1977 ◽

Vol 5 (3) ◽

pp. 319-350 ◽

Cited By ~ 38

Author(s):

Walter Philipp

Keyword(s):

Empirical Distribution ◽

Random Variables ◽

Distribution Functions ◽

Dependent Random Variables ◽

Iterated Logarithm ◽

Weakly Dependent Random Variables ◽

Weakly Dependent ◽

Empirical Distribution Functions

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Limit theorems for weakly dependent random variables

Lithuanian Mathematical Journal ◽

10.1007/bf00967184 ◽

1981 ◽

Vol 20 (3) ◽

pp. 244-249 ◽

Cited By ~ 1

Author(s):

R. Lapinskas

Keyword(s):

Limit Theorems ◽

Random Variables ◽

Dependent Random Variables ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Matrix Transformations of Weakly Dependent Random Variables

Journal of the London Mathematical Society ◽

10.1112/jlms/s2-27.1.185 ◽

1983 ◽

Vol s2-27 (1) ◽

pp. 185-192

Author(s):

Ferenc Móricz

Keyword(s):

Random Variables ◽

Matrix Transformations ◽

Dependent Random Variables ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Some limit theorems for the maximum of normalized sums of weakly dependent random variables

Proceedings of the Third Japan — USSR Symposium on Probability Theory - Lecture Notes in Mathematics ◽

10.1007/bfb0077509 ◽

1976 ◽

pp. 467-474 ◽

Cited By ~ 7

Author(s):

Hiroshi Oodaira

Keyword(s):

Limit Theorems ◽

Random Variables ◽

Dependent Random Variables ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Asymptotic Behavior of the Variance, Estimates on the Moments and Some Probabilistic Inequalities for Sums of Weakly Dependent Random Variables

Limit Theorems and Some Applications in Statistical Physics - TEUBNER-TEXTE zur Mathematik ◽

10.1007/978-3-322-93432-1_3 ◽

1991 ◽

pp. 40-65

Author(s):

Boris Nahapetian

Keyword(s):

Asymptotic Behavior ◽

Random Variables ◽

Dependent Random Variables ◽

Weakly Dependent Random Variables ◽

Probabilistic Inequalities ◽

Variance Estimates ◽

Weakly Dependent

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On the Rate of Convergence in the Central Limit Theorem for Weakly Dependent Random Variables

Theory of Probability and Its Applications ◽

10.1137/1136092 ◽

1992 ◽

Vol 36 (4) ◽

pp. 783-792 ◽

Cited By ~ 7

Author(s):

T. M. Zuparov

Keyword(s):

Central Limit Theorem ◽

Limit Theorem ◽

Rate Of Convergence ◽

Central Limit ◽

Random Variables ◽

Dependent Random Variables ◽

The Central Limit Theorem ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Approximation of distributions of sums of weakly dependent random variables by the normal distribution

Lithuanian Mathematical Journal ◽

10.1007/bf00966266 ◽

1988 ◽

Vol 27 (4) ◽

pp. 359-368 ◽

Cited By ~ 1

Author(s):

J. Sunklodas

Keyword(s):

Normal Distribution ◽

Random Variables ◽

Dependent Random Variables ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Estimate of the convergence rate in the central limit theorem for weakly dependent random variables

Lithuanian Mathematical Journal ◽

10.1007/bf01053921 ◽

1977 ◽

Vol 17 (3) ◽

pp. 313-320 ◽

Cited By ~ 1

Author(s):

J. Sunklodas

Keyword(s):

Central Limit Theorem ◽

Limit Theorem ◽

Convergence Rate ◽

Central Limit ◽

Random Variables ◽

Dependent Random Variables ◽

The Central Limit Theorem ◽

Weakly Dependent Random Variables ◽

Weakly Dependent

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Moment bounds for non-stationary dependent sequences

Journal of Applied Probability ◽

10.2307/3215151 ◽

1994 ◽

Vol 31 (3) ◽

pp. 731-742 ◽

Cited By ~ 15

Author(s):

Tae Yoon Kim

Keyword(s):

Random Variables ◽

Partial Sums ◽

Unified Approach ◽

Dependent Random Variables ◽

Combinatorial Argument ◽

Stationarity Condition ◽

Weakly Dependent Random Variables ◽

Mixing Sequences ◽

Moment Bounds ◽

Weakly Dependent

We provide a unified approach for establishing even-moment bounds for partial sums for a class of weakly dependent random variables satisfying a stationarity condition. As applications, we discuss moment bounds for various types of mixing sequences. To obtain even-moment bounds, we use a ‘combinatorial argument' developed by Cox and Kim (1990).

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