Optimally Dense Packings of Hyperbolic Space

In previous work, a probabilistic approach to controlling difficulties of density in hyperbolic space led to a workable notion of optimal density for packings of bodies. In this paper we extend an ergodic theorem of Nevo to provide an appropriate definition of those packings to be considered optimally dense. Examples are given to illustrate various aspects of the density problem, in particular the shift in emphasis from the analysis of individual packings to spaces of packings.     

Molar extinction coefficients of solutions of some organic compounds

Molar extinction coefficients of aqueous solutions of some organic compounds, viz. formamide (CH₃NO),N-methylformamide (C₂H₅NO),NN-dimethylformamide (C₃H₇NO),NN-dimethylacetamide (C₄H₉NO), 1,4-dioxane (C₄H₈O₂₄), succinimide (C₄H₅NO₂) and solutions of acetamide (C₂H₅NO) and benzoic acid (C₇H₆O₂) in 1,4-dioxane (C₄H₈O₂) have been determined by narrow beam γ-ray transmission method at 81, 356, 511, 662, 1173 and 1332 keV. The experimental values of mass attenuation coefficients of these compounds have been used to calculate effective atomic numbers and electron densities. The additivity rule earlier used for aqueous solution has been extended to non-aqueous (1,4-dioxane) solutions.     

Sequential Estimation of the Parameters in a Trigonometric Regression Model with the Gaussian Coloured Noise

This paper considers the estimation problem for a trigonometric regression model with the noise specified by the Ornstein–Uhlenbeck process with unknown parameter. We propose a sequential procedure which ensures a prescribed mean square precision uniformly in the nuisance parameter. The asymptotic behaviour of the procedure duration mean has been studied. This revised version was published online in August 2006 with corrections to the Cover Date.     

Regression quantiles for unstable autoregressive models

This paper investigates regression quantiles (RQ) for unstable autoregressive models. The uniform Bahadur representation of the RQ process is obtained. The joint asymptotic distribution of the RQ process is derived in a unified manner for all types of characteristic roots on or outside the unit circle. It involves stochastic integrals in terms of a sequence of independent and identically distributed multivariate Brownian motions with correlated components. The related L-estimator is also discussed. The asymptotic distributions of the RQ and the L-estimator corresponding to the nonstationary componentwise arguments can be transformed into a function of a normal random variable and a sequence of i.i.d. univariate Brownian motions. This is different from the analysis based on the LSE in the literature. As an auxiliary theorem, a weak convergence of a randomly weighted residual empirical process to the stochastic integral of a Kiefer process is established. The results obtained in this paper provide an asymptotic theory for nonstationary time series processes, which can be used to construct robust unit root tests.     

Quantum-Chemical Study of the Profiles of Reactions that Form Pyrrole Anion N-Adducts with CS<Subscript>2</Subscript> and CO<Subscript>2</Subscript>

The profiles of reactions leading to pyrrole anion N-adducts with CO₂ and CS₂ have been studied by the ab initio (RHF/6-31+G*, MP2/6-31+G*) and density functional (B3LYP/6-31+G*) methods. Addition of the pyrrole anion to the carbon disulfide molecule is accompanied by the appearance of a minimum corresponding to a pre-reaction complex. The transformation of the complex to the N-pyrrolyldithiocarboxylate anion occurs via a low activation barrier, which is due to repolarization of the C=S bonds. The profile of the reaction leading to the pyrrole anion N-adduct with CO₂ does not contain any intermediate stationary points throughout the whole route from reagents to products.Original Russian Text Copyright © 2004 by V. B. Kobychev, N. M. Vitkovskaya, I. L. Zaitseva, and B. A. Trofimov__________Translated from Zhurnal Strukturnoi Khimii, Vol. 45, No. 6, pp. 990–993, November–December, 2004.     

Nonparametric estimation of distributions with categorical and continuous data

In this paper we consider the problem of estimating an unknown joint distribution which is defined over mixed discrete and continuous variables. A nonparametric kernel approach is proposed with smoothing parameters obtained from the cross-validated minimization of the estimator's integrated squared error. We derive the rate of convergence of the cross-validated smoothing parameters to their ‘benchmark’ optimal values, and we also establish the asymptotic normality of the resulting nonparametric kernel density estimator. Monte Carlo simulations illustrate that the proposed estimator performs substantially better than the conventional nonparametric frequency estimator in a range of settings. The simulations also demonstrate that the proposed approach does not suffer from known limitations of the likelihood cross-validation method which breaks down with commonly used kernels when the continuous variables are drawn from fat-tailed distributions. An empirical application demonstrates that the proposed method can yield superior predictions relative to commonly used parametric models.