Recent Advances in the Central Limit Theorem and Its Weak Invariance Principle for Mixing Sequences of Random Variables (A Survey)

Author(s):  
Magda Peligrad
2021 ◽  
Vol 36 (2) ◽  
pp. 243-255
Author(s):  
Wei Liu ◽  
Yong Zhang

AbstractIn this paper, we investigate the central limit theorem and the invariance principle for linear processes generated by a new notion of independently and identically distributed (IID) random variables for sub-linear expectations initiated by Peng [19]. It turns out that these theorems are natural and fairly neat extensions of the classical Kolmogorov’s central limit theorem and invariance principle to the case where probability measures are no longer additive.


2004 ◽  
Vol 04 (01) ◽  
pp. 1-13 ◽  
Author(s):  
IAN MELBOURNE ◽  
MATTHEW NICOL

We show that statistical limit laws for ergodic stationary sequences of G-equivariant observations ϕ on a probability space Ω×G are inherited by sequences of observations ϕ(·,g0) on the probability space Ω for each fixed g0. The statistical limit laws we consider are the central limit theorem, weak invariance principle and the law of the iterated logarithm.


1972 ◽  
Vol 12 (4) ◽  
pp. 183-194
Author(s):  
V. Paulauskas

The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: В. Паулаускас. Оценка скорости сходимости в центральной предельной теореме для разнораспределенных слагаемых V. Paulauskas. Konvergavimo greičio įvertinimas centrinėje ribinėje teoremoje nevienodai pasiskirsčiusiems dėmenims


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mingzhou Xu ◽  
Kun Cheng

By an inequality of partial sum and uniform convergence of the central limit theorem under sublinear expectations, we establish precise asymptotics in the law of the iterated logarithm for independent and identically distributed random variables under sublinear expectations.


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