We consider asymptotic distributions of maximum deviations of sample covariance matrices, a fundamental problem in high-dimensional inference of covariances. Under mild dependence conditions on the entries of the data matrices, we establish the Gumbel convergence of the maximum deviations. Our...

The paper concerns testing long memory for fractionally integrated nonlinear processes. We show that the exact local asymptotic power is of order O[(logn)-1] for four popular nonparametric tests and is O(m-1/2), where m is the bandwidth which is allowed to grow as fast as n[kappa], [kappa][set...

Covariances play a fundamental role in the theory of stationary processes and they can naturally be estimated by sample covariances. There is a well-developed asymptotic theory for sample covariances of linear processes. For nonlinear processes, however, many important problems on their...

We consider kernel density and regression estimation for a wide class of nonlinear time series models. Asymptotic normality and uniform rates of convergence of kernel estimators are established under mild regularity conditions. Our theory is developed under the new framework of predictive...

We consider asymptotic properties of curve-crossing counts of linear processes and nonlinear time series by curves. Central limit theorems are obtained for curve-crossing counts of short-range dependent processes. For the long-range dependence case, the asymptotic distributions are shown to be...

We present two general results that can be used to obtain asymptotic properties for statistical functionals based on linear long-memory sequences. As examples for the first one we consider L- and V-statistics, in particular tail-dependent L-statistics as well as V-statistics with unbounded...

The paper considers the block sampling method for long-range dependent processes. Our theory generalizes earlier ones by Hall et al. (1998) [11] on functionals of Gaussian processes and Nordman and Lahiri (2005) [16] on linear processes. In particular, we allow nonlinear transforms of linear...

This paper establishes a central limit theorem and an invariance principle for a wide class of stationary random fields under natural and easily verifiable conditions. More precisely, we deal with random fields of the form Xk=g(εk−s,s∈Zd), k∈Zd, where (εi)i∈Zd are iid random variables...