Narrow Results

A measurement of the production cross-section at with the DØ detector at the Fermilab Tevatron, based on the application of a multivariate topological method is presented. The integrated luminosity corresponds to 143.9 pb^-1 in the μ+jets and 141.2 pb^-1 in the e+jets channel. The preliminary cross-section is measured to be .

It is a crucial topic to identify the cross-correlations between time series in multivariate systems. In this paper, we extend the detrended cross-correlation analysis (DCCA) into the multivariate systems, assigned multivariate detrended cross-correlation analysis (MVDCCA). Numerical simulations of synthetic multivariate time series generated by two-exponent and mix-correlated ARFIMA processes are applied to illustrate the validity of the proposed MVDCCA. Results show that the external coupling parameter determines the strength of cross-correlation no matter that it is inter-independent or correlated among channels in a certain multivariate time series. The MVDCCA method is robust enough to detect the scale properties of time series by estimating the Hurst exponent. And we use cross-correlation coefficient to quantify the level of cross-correlations clearly. Furthermore, the MVDCCA method performs well when applied to the stock markets combining the stock daily price returns and trading volume of stock indices. By comparing results only using stock daily price returns in published literatures, we find that the higher recognizability between the pair stock indices can be observed whatever from the same regions or different regions in multivariate situations and the conclusions are more comprehensive.

An algorithm that provides direct, efficient ND polynomial factorization is presented to solve the numerical issues that arise during the direct inversion of helium atom scattering (HAS) diffraction spectra. For an n-variate polynomial the algorithm directly deflates the polynomial to n-single variable equations by evaluating the ratio of pairs of polynomial coefficients. Error estimation of the coefficients of the 1D polynomials is then performed automatically using standard 1D search techniques. The effectiveness of the technique is demonstrated against bi- and trivariate polynomials and an approximate range of validity for error prone polynomials is demonstrated. To demonstrate the effectiveness of the technique, HAS diffraction spectra for the low coverage (2 × 1)-H/Pd(311) system have been analyzed using direct inversion and have revealed that H binds in a three-fold hollow site.

We present a quasi-analytic perturbation expansion for multivariate N-dimensional Gaussian integrals. The perturbation expansion is an infinite series of lower-dimensional integrals (one-dimensional in the simplest approximation). This perturbative idea can also be applied to multivariate Student-t integrals. We evaluate the perturbation expansion explicitly through 2nd order, and discuss the convergence, including enhancement using Padé approximants. Brief comments on potential applications in finance are given, including options, models for credit risk and derivatives, and correlation sensitivities.

In this paper, we study a new class of dynamic risk measures for processes. These new risk measures are derived for the portfolio vectors and adapted to a given filtration. By introducing the notion of discount factor, we deeply analyze the property of cash sub-additivity for these risk measures. Furthermore, by expanding the risk position space, we derive the relationship between these new risk measures and the dynamic convex risk measures introduced by Wei & Hu [(2014) Coherent and convex risk measures for portfolios with applications, Statistics & Probability Letters 90, 114–120]. At last, we establish the representation results for them by making full use of the relationship.

To evaluate the safety of casing string is an important task in the oil exploitation. In this paper, the casing string with complex environment is investigated and the global sensitivity analysis (SA) technique is employed to identify the influential factors on the safety. Since the damage of casing string is of different kinds, three failure modes are mainly considered in the analysis. Then, the multivariate global SA technique is employed to identify the influential factors for the three failure modes simultaneously. Due to the full-size FE analysis of casing string which involves contact analysis of tread, being computationally expensive, a simplified model with full constraints are constructed. Then, to compute the multivariate global sensitivity efficiently, the neural network which is used to surrogate the FE model is employed to perform SA.

In this paper, the notion for orthogonal vector-valued multivariate wavelet packets of space L²(Rⁿ,C^s) is introduced. A method of the construction of orthogonal vector-valued wavelet packets associated with quantity dilation matrix aI (2 ≤ a ∈ Z) is presented. The properties for vector-valued multivariate wavelet packets have been investigated. Finally, a new orthogonal basis of L²(Rⁿ,C^s) is derived from the orthogonal multivariate vector-valued wavelet packets.

In this paper, the notion of orthogonal vector-valued wavelet packets of space L²(Rⁿ,C^s) is introduced. A procedure for constructing orthogonal vector-valued wavelet packets associated with dilation matrix 2I is presented. The properties of vector-valued wavelet packets in higher dimensions have been investigated. Finally, a new orthogonal basis of L²(Rⁿ,C^s) is obtained from the orthogonal vector-valued wavelet packets in higher dimensions.