Microlensing Constraints on Broad Absorption and Emission Line Flows in the Quasar H1413+117

We present new integral field spectroscopy of the gravitationally lensed broad absorption line (BAL) quasar H1413+117, covering the ultraviolet to visible rest-frame spectral range. We observe strong microlensing signatures in lensed image D, and we use this microlensing to simultaneously constrain both the broad emission and broad absorption line gas. By modeling the lens system over the range of probable lensing galaxy redshifts and using on a new argument based on the wavelength-independence of the broad line lensing magnifications, we determine that there is no significant broad line emission from smaller than ~20 light days. We also perform spectral decomposition to derive the intrinsic broad emission line (BEL) and continuum spectrum, subject to BAL absorption. We also reconstruct the intrinsic BAL absorption profile, whose features allow us to constrain outflow kinematics in the context of a disk-wind model. We find a very sharp, blueshifted onset of absorption of 1,500 km/s in both C IV and N V that may correspond to an inner edge of a disk-wind's radial outflow. The lower ionization Si IV and Al III have higher-velocity absorption onsets, consistent with a decreasing ionization parameter with radius in an accelerating outflow. There is evidence of strong absorption in the BEL component which indicates a high covering factor for absorption over two orders of magnitude in outflow radius.Comment: 29 pages, 8 figure

Stochastic approximation of score functions for Gaussian processes

We discuss the statistical properties of a recently introduced unbiased stochastic approximation to the score equations for maximum likelihood calculation for Gaussian processes. Under certain conditions, including bounded condition number of the covariance matrix, the approach achieves $O(n)$ storage and nearly $O(n)$ computational effort per optimization step, where $n$ is the number of data sites. Here, we prove that if the condition number of the covariance matrix is bounded, then the approximate score equations are nearly optimal in a well-defined sense. Therefore, not only is the approximation efficient to compute, but it also has comparable statistical properties to the exact maximum likelihood estimates. We discuss a modification of the stochastic approximation in which design elements of the stochastic terms mimic patterns from a $2^n$ factorial design. We prove these designs are always at least as good as the unstructured design, and we demonstrate through simulation that they can produce a substantial improvement over random designs. Our findings are validated by numerical experiments on simulated data sets of up to 1 million observations. We apply the approach to fit a space-time model to over 80,000 observations of total column ozone contained in the latitude band $40^{\circ}$-$50^{\circ}$N during April 2012.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS627 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Enlargement and Eurozone: Convergence or Divergence

This paper investigates empirically the extent to which the ten new countries of the recent EU enlargement are ready to join the European Monetary Union (EMU). We assess the prospects of successful accession into the EMU using cointegration and common trends analysis on the nominal convergence criteria specified by the Maastricht Treaty as well as on real exchange rates and real per capita GDPs. The empirical results indicate that the enlargement countries are partially ready to join the Eurozone, and need further adjustments in their government policies to be fully prepared for joining the EMU.Economic Integration, EU Enlargement, Cointegration, Common Trends.

Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes

This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a sufficiently large neighborhood of known samples. The estimates of the unknown samples are obtained by minimizing the sum of squares of the residual errors that involve estimates of the autoregressive parameters. A statistical analysis shows that, for a burst of lost samples, the expected quadratic interpolation error per sample converges to the signal variance when the burst length tends to infinity. The method is in fact the first step of an iterative algorithm, in which in each iteration step the current estimates of the missing samples are used to compute the new estimates. Furthermore, the feasibility of implementation in hardware for real-time use is established. The method has been tested on artificially generated auto-regressive processes as well as on digitized music and speech signals

Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA

In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this pa- per, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ the L1-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection