## Let {x,x~ ; n >\\_ 1} be a sequence of i.i.d, random variables. Set Sn = X1 + X2 + β’ .. +Xn and M,~ = maxk 1. By using the strong approximation method, we obtain that for any -1 if and only if EX = 0 and EX 2 < oo, which strengthen and extend the result of Gut and Sp~taru [1], where N is the stan
A law of iterated logarithm for the wavelet transforms of i.i.d. random variables
β Scribed by Haiyan Cai
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 106 KB
- Volume
- 60
- Category
- Article
- ISSN
- 0167-7152
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β¦ Synopsis
We apply a general result on the law of iterated logarithm to the wavelet transforms of i.i.d. random variables and show that a version of this law holds under some regularity conditions on the wavelet. This result provides asymptotic estimates of the rate of decay of the wavelet coe cients at intermediate scaling levels.
π SIMILAR VOLUMES
Let {X n ; n ΒΏ 0} be a sequence of negatively associated random variables. we consider its geometrically weighted series (ΓΏ) = β n = 0 ΓΏ n X n for 0 Β‘ ΓΏ Β‘ 1 and establish the LIL for (ΓΏ) as ΓΏ 1.
Strong laws of large numbers are established for the weighted sums of i.i.d. random variables which have higher-order moment condition. One of the results of Bai and Cheng (2000, Statist. Probab. Lett. 46, 105 -112) is extended.