scholarly journals The performance bounds of learning machines based on exponentially strongly mixing sequences

2007 ◽  
Vol 53 (7) ◽  
pp. 1050-1058 ◽  
Author(s):  
Bin Zou ◽  
Luoqing Li
Keyword(s):  
Performance Bounds ◽  
Learning Machines ◽  
Strongly Mixing ◽  
Mixing Sequences

scholarly journals Limiting behavior of the perturbed empirical distribution functions evaluated at U-statistics for strongly mixing sequences of random variables

1997 ◽  
Vol 10 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Shan Sun ◽  
Ching-Yuan Chiang
Keyword(s):  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Random Variables ◽  
Distribution Functions ◽  
Limiting Behavior ◽  
U Statistics ◽  
Strongly Mixing ◽  
Almost Sure Representation ◽  
Mixing Sequences ◽  
Empirical Distribution Functions

We prove the almost sure representation, a law of the iterated logarithm and an invariance principle for the statistic Fˆn(Un) for a class of strongly mixing sequences of random variables {Xi,i≥1}. Stationarity is not assumed. Here Fˆn is the perturbed empirical distribution function and Un is a U-statistic based on X1,…,Xn.

scholarly journals On the accuracy of bootstrapping sample quantiles of strongly mixing sequences

2007 ◽  
Vol 82 (2) ◽  
pp. 263-282 ◽  
Author(s):  
Shuxia Sun
Keyword(s):  
Rate Of Convergence ◽  
Marginal Distribution ◽  
Regularity Conditions ◽  
One Dimensional ◽  
Strongly Mixing ◽  
Moving Block Bootstrap ◽  
Mixing Sequences ◽  
The One ◽  
Approximations To Distributions ◽  
Sample Quantiles

AbstractIn this paper, we examine the rate of convergence of moving block bootstrap (MBB) approximations to the distributions of normalized sample quantiles based on strongly mixing observations. Under suitable smoothness and regularity conditions on the one-dimensional marginal distribution function, the rate of convergence of the MBB approximations to distributions of centered and scaled sample quantiles is of order O(n−1¼ log logn).

Application to the Uniform Laws of Large Numbers for Dependent Processes

2019 ◽  
pp. 438-447
Author(s):  
Florence Merlevède ◽  
Magda Peligrad ◽  
Sergey Utev
Keyword(s):  
Random Variables ◽  
Rates Of Convergence ◽  
Partial Sums ◽  
Laws Of Large Numbers ◽  
Large Numbers ◽  
Single Function ◽  
Strongly Mixing ◽  
Mixing Coefficients ◽  
Mixing Sequences ◽  
Dependent Processes

As mentioned in Chapter 5, one of the most powerful techniques to derive limit theorems for partial sums associated with a sequence of random variables which is mixing in some sense is the coupling of the initial sequence by an independent one having the same marginal. In this chapter, we shall see how the coupling results mentioned in Section 5.1.3 are very useful to derive uniform laws of large numbers for mixing sequences. The uniform laws of large numbers extend the classical laws of large numbers from a single function to a collection of such functions. We shall address this question for sequences of random variables that are either absolutely regular, or ϕ‎-mixing, or strongly mixing. In all the obtained results, no condition is imposed on the rates of convergence to zero of the mixing coefficients.

On the second Borel-Cantelli lemma for strongly mixing sequences of events

1997 ◽  
Vol 34 (02) ◽  
pp. 381-394 ◽  
Author(s):  
Dirk Tasche
Keyword(s):  
Law Of Large Numbers ◽  
Exponential Rate ◽  
Cantelli Lemma ◽  
Mixing Rate ◽  
Strong Law ◽  
Moment Conditions ◽  
Sequence Of Events ◽  
Large Numbers ◽  
Strongly Mixing ◽  
Mixing Sequences

Assume a given sequence of events to be strongly mixing at a polynomial or exponential rate. We show that the conclusion of the second Borel-Cantelli lemma holds if the series of the probabilities of the events diverges at a certain rate depending on the mixing rate of the events. An application to necessary moment conditions for the strong law of large numbers is given.

On the second Borel-Cantelli lemma for strongly mixing sequences of events

1997 ◽  
Vol 34 (2) ◽  
pp. 381-394 ◽  
Author(s):  
Dirk Tasche
Keyword(s):  
Law Of Large Numbers ◽  
Exponential Rate ◽  
Cantelli Lemma ◽  
Mixing Rate ◽  
Strong Law ◽  
Moment Conditions ◽  
Sequence Of Events ◽  
Large Numbers ◽  
Strongly Mixing ◽  
Mixing Sequences

Assume a given sequence of events to be strongly mixing at a polynomial or exponential rate. We show that the conclusion of the second Borel-Cantelli lemma holds if the series of the probabilities of the events diverges at a certain rate depending on the mixing rate of the events. An application to necessary moment conditions for the strong law of large numbers is given.

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