Given a sequence of fractional frequency deviates, we investigate the relationship between the sample variance of these deviates and the total variance (Totvar) estimator of the Allan variance. We demonstrate that we can recover exactly twice the sample variance by renormalizing the Totvar estimator and then summing it over dyadic averaging times 1, 2, 4, . . . , 2 along with one additional ter...

[1] We use limb profiles to quantify the long‐wavelength topography of the Saturnian satellites. The degree 2 shapes of Mimas, Enceladus, and Tethys are not consistent with hydrostatic equilibrium. We derive 2‐D topographic maps out to spherical harmonic degree 8. There is a good correlation with topography derived from stereo techniques. If uncompensated, topography at degree 3 and higher is l...

The availability of SNP chips for massive genotyping has proven to be useful to genetically characterize populations of domestic cattle and to assess their degree of divergence. In this study, the Illumina BovineHD BeadChip genotyping array was used to describe the genetic variability and divergence among 7 important autochthonous Spanish beef cattle breeds. The within-breed genetic diversity, ...

Hodges & Sargent (2001) developed a measure of a hierarchical model's complexity, degrees of freedom (DF), that is consistent with definitions for scatterplot smoothers, interpretable in terms of simple models, and that enables control of a fit's complexity by means of a prior distribution on complexity. DF describes complexity of the whole fitted model but in general it is unclear how to alloc...

We determine the asymptotics for variance of number zeros random linear combinations orthogonal polynomials degree ≤n in subintervals [a,b] support underlying orthogonality measure μ. show that, as n→∞, this is asymptotic to cn, some explicit constant c>0.

in this paper, it was shown that , where  and , and , where  is not prime and , are od-characterizable.

let $g$ be a connected graph with vertex set $v(g)$‎. ‎the‎ ‎degree resistance distance of $g$ is defined as $d_r(g) = sum_{{u‎,‎v} subseteq v(g)} [d(u)+d(v)] r(u,v)$‎, ‎where $d(u)$ is the degree‎ ‎of vertex $u$‎, ‎and $r(u,v)$ denotes the resistance distance between‎ ‎$u$ and $v$‎. ‎in this paper‎, ‎we characterize $n$-vertex unicyclic‎ ‎graphs having minimum and second minimum degree resista...

Abstract In this paper we introduce a new criterion in order to measure the variance and covariance risks in financial markets. In an asset allocation framework with stochastic (co)variances, we consider the possibility to invest also in variance swaps, that are assets which span the volatility as well as the co-volatility risks. We provide explicit solutions for the portfolio optimization prob...

Stein’s method is used to obtain two theorems on multivariate normal approximation. Our main theorem, Theorem 1.2, provides a bound on the distance to normality for any nonnegative random vector. Theorem 1.2 requires multivariate size bias coupling, which we discuss in studying the approximation of distributions of sums of dependent random vectors. In the univariate case, we briefly illustrate ...

Wavelets have recently been a subject of great interest in geophysics, mathematics and signal processing. The discrete wavelet transform can be used to decompose a time series with respect to a set of basis functions, each one of which is associated with a particular scale. The properties of a time series at different scales can then be summarized by the wavelet variance, which decomposes the v...