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Donatas Surgailis

Publications and source records attributed to Donatas Surgailis.

33 records · Page 2Linked to original sources
arXiv2015

Anisotropic scaling of random grain model with application to network traffic

We obtain a complete description of anisotropic scaling limits of random grain model on the plane with heavy tailed grain area distribution. The scaling limits have either independent or completely dependent increments along one or both coordinate axes and include stable, Gaussian and some `intermediate' infinitely divisible random fields. Asymptotic form of the covariance function of the random grain model is obtained. Application to superposed network traffic is included.

math.PR↗
arXiv2015

A nonlinear model for long memory conditional heteroscedasticity

We discuss a class of conditionally heteroscedastic time series models satisfying the equation $r_t= ζ_t σ_t$, where $ζ_t$ are standardized i.i.d. r.v.'s and the conditional standard deviation $σ_t$ is a nonlinear function $Q$ of inhomogeneous linear combination of past values $r_s, s<t$ with coefficients $b_j$. The existence of stationary solution $r_t$ with finite $p$th moment, $0< p < \infty $ is obtained under some conditions on $Q, b_j$ and $p$th moment of $ζ_0$. Weak dependence properties of $r_t$ are studied, including the invariance principle for partial sums of Lipschitz functions of $r_t$. In the case of quadratic $Q^2$, we prove that $r_t$ can exhibit a leverage effect and long memory, in the sense that the squared process $r^2_t$ has long memory autocorrelation and its normalized partial sums process converges to a fractional Brownian motion.

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arXiv2015

Quasi-MLE for quadratic ARCH model with long memory

We discuss parametric quasi-maximum likelihood estimation for quadratic ARCH process with long memory introduced in Doukhan et al. (2015) and Grublytė and Škarnulis (2015) with conditional variance given by a strictly positive quadratic form of observable stationary sequence. We prove consistency and asymptotic normality of the corresponding QMLE estimates, including the estimate of long memory parameter $0< d < 1/2$. A simulation study of empirical MSE is included.

math.ST↗
arXiv2014

Scaling transition for long-range dependent Gaussian random fields

In Puplinskaite and Surgailis (2014) we introduced the notion of scaling transition for stationary random fields $X$ on $\mathbb{Z}^2$ in terms of partial sums limits, or scaling limits, of $X$ over rectangles whose sides grow at possibly different rate. The present paper establishes the existence of scaling transition for a natural class of stationary Gaussian random fields on $\mathbb{Z}^2$ with long-range dependence. The scaling limits of such random fields are identified and characterized by dependence properties of rectangular increments.

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arXiv2013

Projective stochastic equations and nonlinear long memory

A projective moving average $\{X_t, t \in \mathbb{Z}\}$ is a Bernoulli shift written as a backward martingale transform of the innovation sequence. We introduce a new class of nonlinear stochastic equations for projective moving averages, termed projective equations, involving a (nonlinear) kernel $Q$ and a linear combination of projections of $X_t$ on "intermediate" lagged innovation subspaces with given coefficients $α_i, β_{i,j}$. The class of such equations include usual moving-average processes and the Volterra series of the LARCH model. Solvability of projective equations is studied, including a nested Volterra series representation of the solution $X_t$. We show that under natural conditions on $Q, α_i, β_{i,j}$, this solution exhibits covariance and distributional long memory, with fractional Brownian motion as the limit of the corresponding partial sums process.

math.ST↗
arXiv2013

Joint temporal and contemporaneous aggregation of random-coefficient AR(1) processes

We discuss joint temporal and contemporaneous aggregation of $N$ independent copies of AR(1) process with random-coefficient $a \in [0,1)$ when $N$ and time scale $n$ increase at different rate. Assuming that $a$ has a density, regularly varying at $a = 1$ with exponent $-1 < β< 1$, different joint limits of normalized aggregated partial sums are shown to exist when $N^{1/(1+β)}/n$ tends to (i) $\infty$, (ii) 0, (iii) $0 < μ< \infty$. The limit process arising under (iii) admits a Poisson integral representation on $(0,\infty) \times C(\mathbb{R})$ and enjoys "intermediate" properties between fractional Brownian motion limit in (i) and sub-Gaussian limit in (ii).

math.ST↗
arXiv2013

Contemporaneous aggregation of triangular array of random-coefficient AR(1) processes

We discuss contemporaneous aggregation of independent copies of a triangular array of random-coefficient AR(1) processes with i.i.d. innovations belonging to the domain of attraction of an infinitely divisible law W. The limiting aggregated process is shown to exist, under general assumptions on W and the mixing distribution, and is represented as a mixed infinitely divisible moving-average. Partial sums process of $ is discussed under the assumption E(W^2) is finite and a mixing density regularly varying at the "unit root" x=1 with exponent β>0. We show that the above partial sums process may exhibit four different limit behaviors depending on βand the Lévy triplet of W. Finally, we study the disaggregation problem in spirit of Leipus et al. (2006) and obtain the weak consistency of the corresponding estimator of the mixing distribution in a suitable L_2-space.

math.ST↗
arXiv2013

Aggregation and long memory: recent developments

It is well-known that the aggregated time series might have very different properties from those of the individual series, in particular, long memory. At the present time, aggregation has become one of the main tools for modelling of long memory processes. We review recent work on contemporaneous aggregation of random-coefficient AR(1) and related models, with particular focus on various long memory properties of the aggregated process.

math.ST↗
arXiv2012

On the mixing structure of stationary increment and self-similar symmetric α-stable processes

Mixed moving average processes appear in the ergodic decomposition of stationary symmetric α-stable (SαS) processes. They correspond to the dissipative part of "deterministic" flows generating SαS processes (Rosinski, 1995). Along these lines we study stationary increment and self-similar SαS processes. Since the classes of stationary increment and self-similar processes can be embedded into the class of stationary processes by the Masani and Lamperti transformations, respectively, we characterize these classes of SαS processes in terms of nonsingular flows and the related cocycles. We illustrate this approach considering various examples of self-similar mixed moving average SαS processes introduced in (Surgailis, Rosinski, Mandrekar and Cambanis, 1992).

math.PR↗
arXiv2012

Detection of non-constant long memory parameter

This article deals with detection of nonconstant long memory parameter in time series. The null hypothesis presumes stationary or nonstationary time series with constant long memory parameter, typically an I(d) series with d>-.5. The alternative corresponds to an increase in persistence and includes in particular an abrupt or gradual change from I(d_1) to I(d_2).

math.ST↗
arXiv2012

Moment bounds and central limit theorems for Gaussian subordinated arrays

A general moment bound for sums of products of Gaussian vector's functions extending the moment bound in Taqqu (1977, Lemma 4.5) is established. A general central limit theorem for triangular arrays of nonlinear functionals of multidimensional non-stationary Gaussian sequences is proved. This theorem extends the previous results of Breuer and Major (1981), Arcones (1994) and others. A Berry-Esseen-type bound in the above-mentioned central limit theorem is derived following Nourdin, Peccati and Podolskij (2011). Two applications of the above results are discussed. The first one refers to the asymptotic behavior of a roughness statistic for continuous-time Gaussian processes and the second one is a central limit theorem satisfied by long memory locally stationary process.

math.ST↗
arXiv2012

Nonparametric estimation of the local Hurst function of multifractional Gaussian processes

A new nonparametric estimator of the local Hurst function of a multifractional Gaussian process based on the increment ratio (IR) statistic is defined. In a general frame, the point-wise and uniform weak and strong consistency and a multidimensional central limit theorem for this estimator are established. Similar results are obtained for a refinement of the generalized quadratic variations (QV) estimator. The example of the multifractional Brownian motion is studied in detail. A simulation study is included showing that the IR-estimator is more accurate than the QV-estimator.

math.ST↗
arXiv2010

Measuring the roughness of random paths by increment ratios

A statistic based on increment ratios (IR) and related to zero crossings of increment sequence is defined and studied for measuring the roughness of random paths. The main advantages of this statistic are robustness to smooth additive and multiplicative trends and applicability to infinite variance processes. The existence of the IR statistic limit (called the IR-roughness below) is closely related to the existence of a tangent process. Three particular cases where the IR-roughness exists and is explicitly computed are considered. Firstly, for a diffusion process with smooth diffusion and drift coefficients, the IR-roughness coincides with the IR-roughness of a Brownian motion and its convergence rate is obtained. Secondly, the case of rough Gaussian processes is studied in detail under general assumptions which do not require stationarity conditions. Thirdly, the IR-roughness of a Lévy process with $α-$stable tangent process is established and can be used to estimate the fractional parameter $α\in (0,2)$ following a central limit theorem.

math.ST↗
arXiv2010

A two-sample test for comparison of long memory parameters

We construct a two-sample test for comparison of long memory parameters based on ratios of two rescaled variance (V/S) statistics studied in [Giraitis L., Leipus, R., Philippe, A., 2006. A test for stationarity versus trends and unit roots for a wide class of dependent errors. Econometric Theory 21, 989--1029]. The two samples have the same length and can be mutually independent or dependent. In the latter case, the test statistic is modified to make it asymptotically free of the long-run correlation coefficient between the samples. To diminish the sensitivity of the test on the choice of the bandwidth parameter, an adaptive formula for the bandwidth parameter is derived using the asymptotic expansion in [Abadir, K., Distaso, W., Giraitis, L., 2009. Two estimators of the long-run variance: Beyond short memory. Journal of Econometrics 150, 56--70]. A simulation study shows that the above choice of bandwidth leads to a good size of our comparison test for most values of fractional and ARMA parameters of the simulated series.

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