a strictly stationary mixing process satisfying the

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A strictly stationary β-mixing process satisfying the ...

Nov 01, 2014  In 1983, N. Herrndorf proved that for a ϕ-mixing sequence satisfying the central limit theorem and lim inf n → ∞ σ n 2 / n > 0, the weak invariance principle takes place.The question whether for strictly stationary sequences with finite second moments and a weaker type (α, β, ρ) of mixing the central limit theorem implies the weak invariance principle remained open.

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A strictly stationary $β$-mixing process satisfying the ...

Apr 30, 2013  In 1983, N. Herrndorf proved that for a $ϕ$-mixing sequence satisfying the central limit theorem and $\\liminf_{n\\to\\infty}\\frac{σ^2_n}n>0$, the weak invariance principle takes place. The question whether for strictly stationary sequences with finite second moments and a weaker type ($α$, $β$, $ρ$) of mixing the central limit theorem implies the weak invariance principle remained open ...

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A strictly stationary $\beta$-mixing process satisfying ...

Apr 01, 2013  In 1983, N. Herrndorf proved that for a $\phi$-mixing sequence satisfying the central limit theorem and $\liminf_{n\to\infty}\frac{\sigma^2_n}n>0$, the weak invariance principle takes place. The question whether for strictly stationary sequences with finite second moments and a weaker type ($\alpha$, $\beta$, $\rho$) of mixing the central limit theorem implies the weak invariance principle ...

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Davide Giraudo, Dalibor Volný To cite this version

A STRICTLY STATIONARY β-MIXING PROCESS SATISFYING THE CENTRAL LIMIT THEOREM BUT NOT THE WEAK INVARIANCE PRINCIPLE DAVIDE GIRAUDO AND DALIBOR VOLNÝ Abstract. In 1983, N. Herrndorf proved that for a φ-mixing sequence satis-fying the central limit theorem and liminfn→∞σ 2 n/n > 0, the weak invariance principle takes place.

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A strictly stationary $\beta$-mixing process satisfying ...

A strictly stationary $\beta$-mixing process satisfying the central limit theorem but not the weak invariance principle . ... The question whether for strictly stationary sequences with finite second moments and a weaker type ($\alpha$, $\beta$, $\rho$) of mixing the central limit theorem implies the weak invariance principle remained open. We ...

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A strictly stationary $β$-mixing process satisfying the ...

Apr 30, 2013  In 1983, N. Herrndorf proved that for a $ϕ$-mixing sequence satisfying the central limit theorem and $\\liminf_{n\\to\\infty}\\frac{σ^2_n}n>0$, the weak invariance principle takes place. The question whether for strictly stationary sequences with finite second moments and a weaker type ($α$, $β$, $ρ$) of mixing the central limit theorem implies the weak invariance principle remained open ...

Get price

Davide Giraudo, Dalibor Volný To cite this version

A STRICTLY STATIONARY β-MIXING PROCESS SATISFYING THE CENTRAL LIMIT THEOREM BUT NOT THE WEAK INVARIANCE PRINCIPLE DAVIDE GIRAUDO AND DALIBOR VOLNÝ Abstract. In 1983, N. Herrndorf proved that for a φ-mixing sequence satis-fying the central limit theorem and liminfn→∞σ 2 n/n > 0, the weak invariance principle takes place.

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A strictly stationary $\beta$-mixing process satisfying ...

A strictly stationary $\beta$-mixing process satisfying the central limit theorem but not the weak invariance principle . ... The question whether for strictly stationary sequences with finite second moments and a weaker type ($\alpha$, $\beta$, $\rho$) of mixing the central limit theorem implies the weak invariance principle remained open. We ...

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THE LAW OF THE ITERATED LOGARITHM FOR STATIONARY

holds for stationary sequences satisfying the uniformly strong mixing condition and Reznik showed in [8] that the one is also valid for stationary processes satis-fying the strong mixing condition. But, the conditions used in [5] and [8] are ... Let the strictly stationary process {#/} satisfy the s.m. condition.

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Stat 8112 Lecture Notes Stationary Stochastic Processes ...

is trivial. Thus for an ergodic strictly stationary stochastic process the Birkho ergodic theorem says X n!a.s. E(X 1); which is the same as the conclusion of the SLLN for IID sequences. 3 Mixing and Mixing Coe cients A strictly stationary stochastic process that is determined by a measure-preserving transformation Tis mixing if lim k!1 Q A\T k(B)

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Lecture 13 Time Series: Stationarity, AR(p) MA(q)

variables, using the concept of mixing and stationarity. Or we can rely on the martingale CLT. RS –EC2 -Lecture 13 4 ... A process is strongly (strictly) stationary if it is a Nth-order stationary process for any N. 2nd order stationaryif Time Series – Stationarity 2 2 1 2 1 2 1 2

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Mixing (mathematics) - Wikipedia

A strictly stationary Markov process is β-mixing if and only if it is an aperiodic recurrent Harris chain. The β-mixing coefficients are always bigger than the α-mixing ones, so if a process is β-mixing it will also be α-mixing. There is no direct relationship between β-mixing and ρ-mixing: neither of

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Strong mixing conditions - Encyclopedia of Mathematics

Apr 04, 2016  There is a vast literature on central limit theory for random fields satisfying various strong mixing conditions. ... There is a large literature on strong mixing properties of strictly stationary linear processes (including strictly stationary ARMA processes and also "non-causal" linear processes and linear random fields) and also of some ...

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A note on moment bounds for strong mixing sequences ...

Jan 27, 1993  For a g > 2 and finite K, E∑ n i=1 X i g ⩽ Kn g/2 (all n ⩾ 1) is obtained for a strictly stationary strong mixing or α-mixing sequence }0X i} under less restrictive conditions than Yokoyama's (1980).Doob's argument (1953) for φ-mixing sequences of r.v.'s can be applied directly to α-mixing sequences and leads to improved results for moment bounds when g > 4.

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Generalization Bounds for Non-stationary Mixing Processes

the process is strictly stationary, then d (t 1;t 2) = 0 for all t 1;t 2 2Z. As a more interesting example, consider a weakly stationary stochastic process. A process Z is weakly stationary if E[Zt] is a constant function of t and E[Zt 1 Zt 2] only depends on t 1 2. If L is a squared loss and a set of linear hypothesis H = fYT t q +17!wY T t q: w2R

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The invariance principle for ϕ-mixing sequences Semantic ...

The invariance principle for ϕ-mixing sequences. SummaryIn this paper we investigate the invariance principle for ϕ-mixing sequences, satisfying restrictions on the variances which are a weak form of stationarity. No mixing rate is assumed. For ϕ-mixing strictly stationary sequences we give a necessary and sufficient condition for the ...

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Learning Theory Estimates with Observations from General ...

This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we mean that many stationary stochastic processes can be included.We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a unified treatment on analyzing the learning schemes with various mixing processes ...

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Research - Davide Giraudo Mathematics

A strictly stationary $\beta$-mixing process satisfying the central limit theorem but not the weak invariance principle (with Dalibor Volny) Stochastic Processes and their Applications: 124 (2014), 3769-3781: PDF: 1: A counter example to the central limit theorem in Hilbert spaces under a strong mixing condition (with Dalibor Volny)

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Research - Davide Giraudo Mathematics

A strictly stationary $\beta$-mixing process satisfying the central limit theorem but not the weak invariance principle (with Dalibor Volny) Stochastic Processes and their Applications: 124 (2014), 3769-3781: PDF: 1: A counter example to the central limit theorem in Hilbert spaces under a strong mixing condition (with Dalibor Volny)

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Lecture 13 Time Series: Stationarity, AR(p) MA(q)

variables, using the concept of mixing and stationarity. Or we can rely on the martingale CLT. RS –EC2 -Lecture 13 4 ... A process is strongly (strictly) stationary if it is a Nth-order stationary process for any N. 2nd order stationaryif Time Series – Stationarity 2 2 1 2 1 2 1 2

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Generalization Bounds for Non-stationary Mixing Processes

the process is strictly stationary, then d (t 1;t 2) = 0 for all t 1;t 2 2Z. As a more interesting example, consider a weakly stationary stochastic process. A process Z is weakly stationary if E[Zt] is a constant function of t and E[Zt 1 Zt 2] only depends on t 1 2. If L is a squared loss and a set of linear hypothesis H = fYT t q +17!wY T t q: w2R

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Strong mixing conditions - Encyclopedia of Mathematics

Apr 04, 2016  There is a vast literature on central limit theory for random fields satisfying various strong mixing conditions. ... There is a large literature on strong mixing properties of strictly stationary linear processes (including strictly stationary ARMA processes and also "non-causal" linear processes and linear random fields) and also of some ...

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The Rosenblatt Process* - Springer

Theorem 2.3 Suppose {Xj} is a strictly stationary sequence with mean zero satisfying the strong mixing condition and condition (2). Then the CLT (4) holds if and only if S2 —^ is uniformly integrable. (5) 0. Rosenblatt's Theorem 2.2 is stated with 8 = 2. For the history behind Theorem 2.3, see [9].

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Inequalities and limit theorems for weakly dependent sequences

inequalities for random variables satisfying mixing conditions and coupling results which ... variables. In order to shorten the exposition, we consider strictly stationary sequences (cf. Rio (1995c) for results in the non stationary case). ... sum process associated to a stationary and strongly mixing

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Stationarity and mixing properties of the dynamic Tobit ...

May 01, 2010  1.. IntroductionThe purpose of this paper is to establish strict stationarity and mixing properties for the dynamic Tobit process of the form (1) y it = max (0, z ′ it ζ 0 + τ 0 y i t − 1 + γ i 0 + ε it) where z it is an exogenous regressor, ε it is a disturbance term and γ i0, τ 0 and ζ 0 are fixed parameters. Tobin (1958) proposed a static version of Model to analyze durable good ...

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Mixing (mathematics) - Wikipedia

A strictly stationary Markov process is β-mixing if and only if it is an aperiodic recurrent Harris chain. The β-mixing coefficients are always bigger than the α-mixing ones, so if a process is β-mixing it will also be α-mixing. There is no direct relationship between β-mixing and ρ-mixing: neither of

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On Strong Mixing Conditions for Stationary Gaussian ...

Jul 28, 2006  (2020) Mixing and moments properties of a non-stationary copula-based Markov process. Communications in Statistics - Theory and Methods 49 :18, 4559-4570. (2020) Semiparametric estimation with spatially correlated recurrent events.

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Stationary Stochastic Process - Purdue University

A process is a Gaussianprocessif its restrictions (zt 1,...,zt m) follow normal distributions. A process zt on T is weaklystationaryof second order if E[zt] = E[z 0] = µ cov[zt,zt+h] = cov[z 0,zh] = γh, for all t,h ∈ T . A Gaussian process that is weakly stationary of second order is also strictly stationary.

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Central limit theorems for Hilbert-space valued random ...

the whole past of the process and just two future observations at a time, by using the Bernstein blocking technique and approximations by martingale differences. This paper will present a central limit theorem for strictly stationary Hilbert-space valued random fields satisfying the ρ′-mixing condition. We proceed by prov-ing in Theorem 3 ...

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