Partial Distance Correlation with Methods for Dissimilarities

Distance covariance and distance correlation are scalar coefficients that characterize independence of random vectors in arbitrary dimension. Properties, extensions, and applications of distance correlation have been discussed in the recent literature, but the problem of defining the partial distance correlation has remained an open question of considerable interest. The problem of partial distance correlation is more complex than partial correlation partly because the squared distance covariance is not an inner product in the usual linear space. For the definition of partial distance correlation we introduce a new Hilbert space where the squared distance covariance is the inner product. We define the partial distance correlation statistics with the help of this Hilbert space, and develop and implement a test for zero partial distance correlation. Our intermediate results provide an unbiased estimator of squared distance covariance, and a neat solution to the problem of distance correlation for dissimilarities rather than distances

Correlation studies on surface particle detection methods

The accurate determination of dust levels on optical surfaces is necessary to assess sensor system performance. A comparison study was made on several particle measurement methods including those based on direct imaging and light scattering. The effectiveness of removing the particles from the surface prior to determining particle size distributions was also assessed. These studies revealed that some methods, especially those requiring particle removal before analysis, are subject to large systematic errors affecting particle size distributions. Thus, an understanding of the particle measurement methods employed is necessary before any surface cleanliness or obstruction value assignments are accepted as true representations of an optical surface contamination condition

Exploring Correlation Methods to Determine QCD beta-Functions on the Lattice

We investigate -- as an alternative to usual Monte Carlo Renormalization Group methods -- the feasibility of extracting QCD beta-functions directly from a lattice analysis of correlations between the action and Wilson loops. We test this correlation technique numerically in four dimensional SU(2) gauge theory, on a 16^4 lattice at beta = 2.5 and find very promising results.Comment: 12 pages, 2 Figure

Correlation methods for the analysis of X-ray polarimetric signals

X-ray polarimetric measurements are based on studying the distribution of the directions of scattered photons or photoelectrons and on the search of a sinusoidal modulation with a period of {\pi}. We developed two tools for investigating these angular distributions based on the correlations between counts in phase bins separated by fixed phase distances. In one case we use the correlation between data separated by half of the bin number (one period) which is expected to give a linear pattern. In the other case, the scatter plot obtained by shifting by 1/8 of the bin number (1/4 of period) transforms the sinusoid in a circular pattern whose radius is equal to the amplitude of the modulation. For unpolarized radiation these plots are reduced to a random point distribution centred at the mean count level. This new methods provide direct visual and simple statistical tools for evaluating the quality of polarization measurements and for estimating the polarization parameters. Furthermore they are useful for investigating distortions due to systematic effects

Relativistic Internally Contracted Multireference Electron Correlation Methods

We report internally contracted relativistic multireference configuration interaction (ic-MRCI), complete active space second-order perturbation (CASPT2), and strongly contracted n-electron valence state perturbation theory (NEVPT2) on the basis of the four-component Dirac Hamiltonian, enabling accurate simulations of relativistic, quasi-degenerate electronic structure of molecules containing transition-metal and heavy elements. Our derivation and implementation of ic-MRCI and CASPT2 are based on an automatic code generator that translates second-quantized ansatze to tensor-based equations, and to efficient computer code. NEVPT2 is derived and implemented manually. The rovibrational transition energies and absorption spectra of HI and TlH are presented to demonstrate the accuracy of these methods

Correlation of Puma airloads: Evaluation of CFD prediction methods

A cooperative program was undertaken by research organizations in England, France, Australia and the U.S. to study the capabilities of computational fluid dynamics codes (CFD) to predict the aerodynamic loading on helicopter rotor blades. The program goal is to compare predictions with experimental data for flight tests of a research Puma helicopter with rectangular and swept tip blades. Two topics are studied. First, computed results from three CFD codes are compared for flight test cases where all three codes use the same partial inflow-angle boundary conditions. Second, one of the CFD codes (FPR) is iteratively coupled with the CAMRAD/JA helicopter performance code. These results are compared with experimental data and with an uncoupled CAMRAD/JA solution. The influence of flow field unsteadiness is found to play an important role in the blade aerodynamics. Alternate boundary conditions are suggested in order to properly model this unsteadiness in the CFD codes

Post-correlation radio frequency interference classification methods

We describe and compare several post-correlation radio frequency interference classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for radio frequency interference mitigation is pressing. We investigated several classification methods and find that, for the data sets we used, the most accurate among them is the SumThreshold method. This is a new method formed from a combination of existing techniques, including a new way of thresholding. This iterative method estimates the astronomical signal by carrying out a surface fit in the time-frequency plane. With a theoretical accuracy of 95% recognition and an approximately 0.1% false probability rate in simple simulated cases, the method is in practice as good as the human eye in finding RFI. In addition it is fast, robust, does not need a data model before it can be executed and works in almost all configurations with its default parameters. The method has been compared using simulated data with several other mitigation techniques, including one based upon the singular value decomposition of the time-frequency matrix, and has shown better results than the rest.Comment: 14 pages, 12 figures (11 in colour). The software that was used in the article can be downloaded from http://www.astro.rug.nl/rfi-software

Systematic analysis of group identification in stock markets

We propose improved methods to identify stock groups using the correlation matrix of stock price changes. By filtering out the marketwide effect and the random noise, we construct the correlation matrix of stock groups in which nontrivial high correlations between stocks are found. Using the filtered correlation matrix, we successfully identify the multiple stock groups without any extra knowledge of the stocks by the optimization of the matrix representation and the percolation approach to the correlation-based network of stocks. These methods drastically reduce the ambiguities while finding stock groups using the eigenvectors of the correlation matrix.Comment: 9 pages, 7 figure