Necessary and sufficient conditions for the strong law of large numbers

The well-known characterization indicated in the title involves the moving maximal dyadic averages of the sequence (X _k : k = 1, 2, …) of random variables in Probability Theory. In the present paper, we offer another characterization of the SLLN which does not require to form any maximum. Instead, it involves only a specially selected sequence of moving averages. The results are also extended for random fields (X _kℓ: k, ℓ = 1, 2, …).     

A new sufficient condition in the invariance principle for the partial sum processes of moving averages

We obtain a new sufficient condition for the C-convergence of distributions of the partial sum processes of moving averages to the distribution of a Wiener process in the metric space D[0,1] with the Skorokhod metric.     

Semiparametric Bootstrap Approach to Hypothesis Tests and Confidence Intervals for the Hurst Coefficient

A major application of rescaled adjusted range analysis (R–S analysis) is to the study of price fluctuations in financial markets. There, the value of the Hurst constant, H, in a time series may be interpreted as an indicator of the irregularity of the price of a commodity, currency or similar quantity. Interval estimation and hypothesis testing for H are central to comparative quantitative analysis. In this paper we propose a new bootstrap, or Monte Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages, autoregressive moving averages and many more. By way of contrast we suggest simulation using a single type of continuous-time process, with its fractal dimension. We provide theoretical justification for this method, and explore its numerical properties and statistical performance by application to real data on commodity prices and exchange rates. This revised version was published online in June 2006 with corrections to the Cover Date.     

Critical Behavior in Almost Sure Central Limit Theory

Let X ₁,X ₂,… be i.i.d. random variables with EX ₁=0, EX ₁²=1 and let S _k =X ₁+⋅⋅⋅+X _k . We study the a.s. convergence of the weighted averages

Régularisation spectrale et propriétés métriques des moyennes mobiles

We develop a spectral regularization technique for moving averages , where ϕ is a nondecreasing map andU: H→H is a contraction of a Hilbert space (H, ‖·‖). We obtain a spectral regularization inequality which allows one to evaluate efficiently the increments ‖B _m ^U , ϕ (f)−B _n ^U , ϕ (f)‖,f∈H, by means of where is a properly regularized version of the spectral measure off with respect toU. We apply this inequality to an investigation of metric properties of the sets of moving averages {B _n ^{U, ϕ} (f), n ∈N} with fixedf∈H andN⊂ N. In particular, we obtain estimates of the associated covering numbers as well as of the related Littlewood-Paley-type square functions. This work extends our previous results concerning the case of classical averages (ϕ(n)=0). Since it is well-known that the structure of general moving averages is more complicated, there is no surprise that the general results we obtain are sometimes less complete than their classical counterparts and need suitable moment assumptions on the spectral measure (depending on the growth of the shift function ϕ). Nevertheless, when applied to the classical situation, our estimates still yield optimal bounds.

---

---

Avec pour le premier author, le soutien de la fondation russe pour la recherche fondamentale, subvention 99-01-00112 et INTAS subvention 99-01317.     

Random Broken Lines That Weakly Converge to a Fractional Ornstein-Uhlenbeck Process

We study the behavior of sequences of continuous random broken lines that are constructed from increased sums of stationary sequences. We obtain conditions of weak convergence of these random broken lines to Gaussian processes of types of a fractional Ornstein-Uhlenbeck process and the fractional Brownian motion. Sequences of moving averages are considered as the main examples of the investigated stationary sequences. Bibliography: 8 titles.__________Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 298, 2003, pp. 134–149.     

A central-limit-theorem version ofL=λw

Underlying the fundamental queueing formulaL=W is a relation between cumulative processes in continuous time (the integral of the queue length process) and in discrete time (the sum of the waiting times of successive customers). Except for remainder terms which usually are asymptotically negligible, each cumulative process is a random time-transformation of the other. As a consequence, in addition to the familiar relation between the with-prob ability-one limits of the averages, roughly speaking, the customer-average wait obeys a central limit theorem if and only if the time-average queue length obeys a central limit theorem, in which case both averages, properly normalized, converge in distribution jointly, and the individual limiting distributions are simply related. This relation between the central limit theorems is conveniently expressed in terms of functional central limit theorems, using the continuous mapping theorem and related arguments. The central limit theorems can be applied to compare the asymptotic efficiency of different estimators of queueing parameters. For example, when the arrival rate is known and the interarrivai times and waiting times are negatively correlated, it is more asymptotically efficient to estimate the long-run time-average queue lengthL indirectly by the sample-average of the waiting times, invokingL=W, than it is to estimate it by the sample-average of the queue length. This variance-reduction principle extends a corresponding result for the standard GI/G/s model established by Carson and Law [2].Supported by the National Science Foundation under Grant No. ECS-8404809 and by the U.S. Army under Contract No. DAAG29-80-C-0041.     

Stable mixed moving averages

Summary The class of (non-Gaussian) stable moving average processes is extended by introducing an appropriate joint randomization of the filter function and of the stable noise, leading to stable mixed moving averages. Their distribution determines a certain combination of the filter function and the mixing measure, leading to a generalization of a theorem of Kanter (1973) for usual moving averages. Stable mixed moving averages contain sums of independent stable moving averages, are ergodic and are not harmonizable. Also a class of stable mixed moving averages is constructed with the reflection positivity property.Research supported by AFSOR Contract 91-0030Research also supported by ARO DAAL-91-G-0176Research also supported by AFOSR 90-0168Research also supported by ONR N00014-91-J-0277     

Pointwise convergence of ergodic averages along cubes

Let (X, B, μ, T) be a measure preserving system. We prove the pointwise convergence of ergodic averages along cubes of 2 ^k − 1 bounded and measurable functions for all k. We show that this result can be derived from estimates about bounded sequences of real numbers and apply these estimates to establish the pointwise convergence of some weighted ergodic averages and ergodic averages along cubes for not necessarily commuting measure preserving transformations.     

Testing a sub-hypothesis in linear regression models with long memory covariates and errors

This paper considers the problem of testing a sub-hypothesis in homoscedastic linear regression models when the covariate and error processes form independent long memory moving averages. The asymptotic null distribution of the likelihood ratio type test based on Whittle quadratic forms is shown to be a chi-square distribution. Additionally, the estimators of the slope parameters obtained by minimizing the Whittle dispersion is seen to be n ^1/2-consistent for all values of the long memory parameters of the design and error processes. Research of the first author was partly supported by the NSF DMS Grant 0701430. Research of the second author was partly supported by the bilateral France-Lithuania scientific project Gilibert and the Lithuanian State Science and Studies Foundation grant T-15/07.     

Reproducing kernel Hilbert spaces and the law of the iterated logarithm for Gaussian processes

In this paper, we find the limit set of a sequence (2 log n)^?1/2 X _n (t), n≧3) of Gaussian processes in C [0,1], where the processes X _n (t) are defined on the same probability space and have the same distribution. Our result generalizes the theorems of Oodaira and Strassen, and we also apply it to obtain limit theorems for stationary Gaussian processes, moving averages of the type \(\int\limits_0^t {f\left( {t - s} \right)dW\left( s \right)} \) , where W(s) is the standard Wiener process, and other Gaussian processes. Using certain properties of the unit ball of the reproducing kernel Hubert space of X _n (t), we derive the usual law of the iterated logarithm for Gaussian processes. The case of multidimensional time is also considered.     

Equivalent martingale measures for Lévy-driven moving averages and related processes

In the present paper we obtain sufficient conditions for the existence of equivalent local martingale measures for Lévy-driven moving averages and other non-Markovian jump processes. The conditions that we obtain are, under mild assumptions, also necessary. For instance, this is the case for moving averages driven by an $\alpha$-stable Lévy process with $\alpha \in (1,2]$.Our proofs rely on various techniques for showing the martingale property of stochastic exponentials.     

Growth Rates in the Quaquaversal Tiling

Conway and Radin's ``quaquaversal' tiling of R ³ is known to exhibit statistical rotational symmetry in the infinite volume limit. A finite patch, however, cannot be perfectly isotropic, and we compute the rates at which the anisotropy scales with size. In a sample of volume N , tiles appear in O(N ^1/6 ) distinct orientations. However, the orientations are not uniformly populated. A small (O(N ^1/84 ) ) set of these orientations account for the majority of the tiles. Furthermore, these orientations are not uniformly distributed on SO(3) . Sample averages of functions on SO(3) seem to approach their ergodic limits as N ^-1/336 . Since even macroscopic patches of a quaquaversal tiling maintain noticeable anisotropy, a hypothetical physical quasicrystal whose structure was similar to the quaquaversal tiling could be identified by anisotropic features of its electron diffraction pattern. Received October 19, 1998, and in revised form March 11, 1999.     

A Quantitative Version of a de Bruijn-Post Theorem

A theorem due to de Bruijn and Post states that if a real valued function f defined on [0, 1] is not Riemann-integrable, then there exists a uniformly distributed sequence { x_i} such that the averages do not admit a limit. In this paper we will prove a quantitative version of this result and we will extend it to functions with values in ℝ^d.     

Averaging sequences and modulated ergodic theorems for weakly almost periodic group representations

LetT be a weakly almost periodic (WAP) representation of a locally compact Σ-compact groupG by linear operators in a Banach spaceX, and letM = M(T) be its ergodic projection onto the space of fixed points (i.e.,Mx is the unique fixed point in the closed convex hull of the orbit ofx). A sequence of probabilities Μ_n is said toaverage T [weakly] if ∫T(t)x dΜ _n converges [weakly] toM(T)x for eachx ∃X. We callΜ _n [weakly]unitarily averaging if it averages [weakly] every unitary representation in a Hilbert space, and [weakly]WAPRaveraging if it averages [weakly] every WAP representation. We investigate some of the relationships of these notions, and connect them with properties of the regular representation (by translations) in the spaceWAP(G). Research partially supported by the Israel Science Foundation.     

Theoremes ergodiques perturbes

We obtain results on almost sure convergence of ergodic averages along arithmetic subsequences perturbed by independent identically distributed random variables having ap ^th finite moment for somep>0. To prove these results, we use methods based on the harmonic analysis and the theory of Gaussian processes. In fact that will express the stability of Bourgain’s results concerning convergence of ergodic averages for certain arithmetic subsequences.      

When the cone condition fails

Summary We study the class of convergence E∪L¹of a family of moving averages which does not satisfy the cone condition. We show that if E₀is a finite subset of Ewhich is (E)-stable for the multiplication operation: f,g∈E₀ →f·g∈E, then the supremum sup { f, f∈E₀} is dominated by sup{ g, g∈G₀}where G₀is a Gaussian family with same covariance function. This is used to derive a maximal inequality for families Fsuch that each finite subset is E-stable and Fis a GB set.     

Limit process of stationary TASEP near the characteristic line

The totally asymmetric simple exclusion process (TASEP) on\input amssym ${\Bbb Z}$ with the Bernoulli‐ρ measure as an initial condition, 0 < ρ < 1, is stationary. It is known that along the characteristic line, the current fluctuates at an order of t^1/3. The limiting distribution has also been obtained explicitly. In this paper we determine the limiting multipoint distribution of the current fluctuations moving away from the characteristics by the order t^2/3. The main tool is the analysis of a related directed last percolation model. We also discuss the process limit in tandem queues in equilibrium. © 2010 Wiley Periodicals, Inc.     

Discrete time parametric models with long memory and infinite variance

We study a large class of infinite variance time series that display long memory. They can be represented as linear processes (infinite order moving averages) with coefficients that decay slowly to zero and with innovations that are in the domain of attraction of a stable distribution with index 1 < α < 2 (stable fractional ARIMA is a particular example). Assume that the coefficients of the linear process depend on an unknown parameter vector β which is to be estimated from a series of length n. We show that a Whittle-type estimator β_n for β is consistent (β_n converges to the true value β₀ in probability as n → ∞), and, under some additional conditions, we characterize the limiting distribution of the rescaled differences (n/logn)^1/ga(β_n − β₀).     

Local ergodic theorems for K-spherical averages on the Heisenberg group

Given a Gelfand pair where is the Heisenberg group and K is a compact subgroup of the unitary group U(n) we consider the sphere and ball averages of certain K-invariant measures on . We prove local ergodic theorems for these measures when . We also consider averages over annuli in the case of reduced Heisenberg group and show that when the functions have zero mean value the maximal function associated to the annulus averages behave better than the spherical maximal function. We use square function arguments which require several properties of the K-spherical functions. Received September 1, 1998; in final form July 9, 1999