From PET to SPLIT Yuri Kifer. PET: Polynomial Ergodic Theorem (Bergelson) preserving and weakly mixing is bounded measurable functions polynomials,integer.

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Presentation transcript:

From PET to SPLIT Yuri Kifer

PET: Polynomial Ergodic Theorem (Bergelson) preserving and weakly mixing is bounded measurable functions polynomials,integer valued on the integers and in

SPLIT: sum product limit theorem Is asymptotically normal: strong mixing conditions are needed WANT TO PROVE that Ifthen Application to multiple recurrence

setup: bounded stationary processes algebras mixing coefficient approximation coefficient Assumption:

Functions q: nonnegative integer valued on integers functions, satisfying

Result: Set then Is asymptotically normal with zero mean and The The the variance where

when variance is zero? either almost surely for some or for all and unitary on, random variable from

Functional CLT For set Then in distribution as Brownian motion

Applications

Basic (splitting) inequalities and are and - measurable, respectively, then Let be bounded random variables and then for where

variance: Set Then relying on estimates of

Some estimates we have and Using splitting inequalities we get that is small if or is large and is small and the limit of the previous slide follows.

more delicate Gaussian estimates Let. Then and

Block technique Set and Define for where

characteristic functions Set and then Set Then (main estimate):

Main estimate: idea of the proof 1 st step:

2 nd step:

3-d step: Then

4 th step: Writing powers of sums as sums of products the estimate of comes down to the estimate of Next, we change the order of products in the two expectations above so that the product appear immediately after the expectation and apply the “splitting” inequality times to the latter product for both expectations.

we obtain and

Next, For each fixed we apply the “splitting estimate” to the product after the expectation and in view of the size of gaps between the blocks we obtain Collecting the above estimates the main estimate follows.

Conclusion of proof: using and Gaussian type estimates above we obtain and, finally,

Concluding remark: the proof does not work when, for instance, Still, if are i.i.d. then is asymptotically normal! This can be proved applying the standard clt for triangular series to where