In studies of hydrodynamic turbulence and nonequilibrium systems, it has been demonstrated that the observed non-Gaussian probability density functions are often described effectively by a superposition of Gaussian distributions with fluctuating variances. Based on this framework, we propose a general method to characterize intermittent and non-Gaussian time series. In our approach, an observed...

Since the introduction of mandatory helmet legislation (MHL) in Australia, debate on the effect of MHL on cyclist head injuries has been ongoing. The debate sometimes revolves around the statistical methodology used to assess intervention effectiveness. Supporters of rescinding the MHL thereby encouraging cyclists to ride without helmets, regularly dismiss statistical evaluations as being flawe...

In this paper, a parallel algorithm is given that, given a graph G = (V;E), decides whether G is a series parallel graph, and if so, builds a decomposition tree for G of series and parallel composition rules. The algorithm uses O(log jEj log jEj) time and O(jEj) operations on an EREW PRAM, and O(log jEj) time and O(jEj) operations on a CRCW PRAM (note that ifG is a simple series parallel graph,...

We give series expansions for the Barnes multiple zeta functions in terms of rational functions whose numerators are complex-order Bernoulli polynomials, and whose denominators are linear. We also derive corresponding rational expansions for Dirichlet L-functions and multiple log gamma functions in terms of higher order Bernoulli polynomials. These expansions naturally express many of the well-...

Theorem 2.1. For all n ≥ 1, n log n− n < log(n!) < n log n, so log(n!) ∼ n log n. Proof. The inequality log(n!) < n log n is a consequence of the trivial inequality n! < nn. Here are three methods of showing n log n− n < log(n!). Method 1: A Riemann sum approximation for ∫ n 1 log x dx using right endpoints is log 2+ · · ·+ log n = log(n!), which overestimates, so log(n!) > ∫ n 1 log x dx = n l...

This paper discusses the use of fractional exponential models (Robinson (1990), Beran (1994)) to model the spectral density f(x) of a covariance stationary process when f(x) may be decomposed as f(x) = x ?2d f (x), where f (x) is bounded and bounded away from zero. A form of log-periodogram regression technique is presented both in the parametric context (i.e. f (x) is a nite order exponential ...

a The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, College of Engineering, The Pennsylvania State University, University Park, PA 16802, United States 329F Information Sciences and Technology Building, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, United States c Faculty of Information Technology, ...

In this paper we consider the problem of estimating high-frequency beta of an asset return when the returns are subject to the effects of market microstructure. Specifically, we study the correlation between intraday log returns of two assets. Our investigation starts with the effect of non-synchronous trading on intraday log returns when the underlying return series follows a stationary time s...

Time series of prices as well as time series based on prices or time series which describe prices and their dynamism are called financial time series. These time series have some typical properties. There are two basic assumptions: normality and linearity of log returns of the financial time series. The distributions of log returns are usually skewed and more peaked that the normal distribution...