Central Limit Theorems and Quadratic Variations in terms of Spectral Density

In [2], the authors present a discrete schema, called the random rewards schema, and show its convergence to a stable self-similar statinonary incre- ments process called

Practical sum-rate spectral efficiency results for the uplink and the downlink channel of a joint-processing cellular system.. than the last-decoded UT signals, since the decoder

Keywords : Markov additive process, central limit theorems, Berry-Esseen bound, Edge- worth expansion, spectral method, ρ-mixing, M -estimator..

An asymptotic formula is given for ergodic integrals in terms of these finitely-additive measures, and, as a corollary, limit theorems are obtained for dynamical systems given

We prove functional central and non-central limit theorems for generalized varia- tions of the anisotropic d-parameter fractional Brownian sheet (fBs) for any natural number d..

In the third part, we observe that Sogge’s estimate on spectral projections is valid for any complete manifold with C ∞ bounded geometry, and in particular for asymptotically

Right plot: Spectral density (black), pointwise mean of the estimates f ˜ n (red) and 95% confidence intervals (green), for n=1000 data points, for Example 1.... Left plot:

The observation times are possibly random, and we try to find conditions on those times, allowing for limit theorems when the number of observations increases (the “high