随机变量和的特征函数的性质与应用

运用随机变量和的特征函数定义研究了随机变量和的特征函数在原点处二阶导数与协方差矩阵的关系,并给了一个简单的应用.     

An uncertainty evaluation for multiple measurements by GUM

An approach for uncertainty evaluation is proposed to determine the overall uncertainty by combining the uncertainties of the individual results from multiple measurements. It is accomplished by the separate combinations of the individual random and systematic components of the uncertainties of the individual results. The approach is useful when the individual results are not statistically different. It is recognized that, owing to the correlation, the uncertainty resulting from systematic effects is not reduced by multiple measurements. On the contrary, the uncertainty resulting from random effects can be reduced. Received: 3 May 2002 Accepted: 16 July 2002     

Non-stationary correlation matrices and noise

The exact meaning of the noise spectrum of eigenvalues of the correlation matrix is discussed. In order to better understand the possible phenomena behind the observed noise, the spectrum of eigenvalues of the correlation matrix is studied under a model where most of the true eigenvalues are zero and the parameters are non-stationary. The results are compared with real observation of Brazilian assets, suggesting that, although the non-stationarity seems to be an important aspect of the problem, partially explaining some of the eigenvalues as well as part of the kurtosis of the assets, it cannot, by itself, provide all the corrections needed to make the proposed model fit the data perfectly.     

On Some Tests of the Covariance Matrix Under General Conditions

We consider the problem of testing the hypothesis about the covariance matrix of random vectors under the assumptions that the underlying distributions are nonnormal and the sample size is moderate. The asymptotic expansions of the null distributions are obtained up to n ^−1/2. It is found that in most cases the null statistics are distributed as a mixture of independent chi-square random variables with degree of freedom one (up to n ^−1/2) and the coefficients of the mixtures are functions of the fourth cumulants of the original random variables. We also provide a general method to approximate such distributions based on a normalization transformation.     

ℓ1-symmetric vector random fields

This paper studies the properties of ${?}_1$-symmetric vector random fields in ${\mathbb{R}}^d$, whose direct/cross covariances are functions of ${?}_1$-norm. The spectral representation and a turning bands expression of the covariance matrix function are derived for an ${?}_1$-symmetric vector random field that is mean square continuous. We also establish an integral relationship between an ${?}_1$-symmetric covariance matrix function and an isotropic one. In addition, a simple but efficient approach is proposed to construct the ${?}_1$-symmetric random field in ${\mathbb{R}}^d$, whose univariate marginal distributions may be taken as arbitrary infinitely divisible distribution with finite variance.     

Non-manifold surface reconstruction from high-dimensional point cloud data

We present an algorithm capable of reconstructing a non-manifold surface embedded as a point cloud in a high-dimensional space. Our algorithm extends a previously developed incremental method and produces a non-optimal triangulation, but will work for non-orientable surfaces, and for surfaces with certain types of self-intersection. The self-intersections must be ordinary double curves and are fitted locally by intersecting planes using a degenerate quadratic surface. We present the algorithm in detail and provide many examples, including a dataset describing molecular conformations of cyclo-octane.     

Lyapunov modes and time-correlation functions for two-dimensional systems

The relation between the Lyapunov modes (delocalized Lyapunov vectors) and the momentum autocorrelation function is discussed in two-dimensional hard-disk systems. We show numerical evidence that the smallest time-oscillating period of the Lyapunov modes is twice as long as the time-oscillating period of momentum autocorrelation function for both square and rectangular two-dimensional systems with hard-wall boundary conditions.     

Impact of the tick-size on financial returns and correlations

We demonstrate that the lowest possible price change (tick-size) has a large impact on the structure of financial return distributions. It induces a microstructure as well as possibly altering the tail behavior. On small return intervals, the tick-size can distort the calculation of correlations. This especially occurs on small return intervals and thus contributes to the decay of the correlation coefficient towards smaller return intervals (Epps effect). We study this behavior within a model and identify the effect in market data. Furthermore, we present a method to compensate this purely statistical error.