Alfven waves as a driving mechanism in stellar winds

Alfven waves have been invoked as an important mechanism of particle acceleration in stellar winds of cool stars. After their identification in the solar wind they started to be studied in winds of stars located in different regions of the FIR diagram. We discuss here some characteristics of these waves and we present a direct application in the acceleration of late-type stellar winds. (C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserved.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Driving forces for the adsorption of cyclopentene on InP(001)

In this work the interaction of cyclopentene with a set of InP(001) surfaces is investigated by means of the density functional theory. We propose a simple approach for evaluating the surface strain and based on it we have found a linear relation between bond and strain energies and the adsorption energy. Our results also indicate that the higher the bond energy, the more disperse the charge distribution is around the adsorption site associated to the high occupied state, a key feature that characterizes the adsorption process. Different adsorption coverages are used to evaluate the proposed equation. Our results suggest that the proposed approach might be extended to other systems where the interaction of the semiconductor surface and the molecule is restricted to first neighbor sites. (C) 2011 Elsevier B.V. All rights reserved.

Driving forces in substitution reactions of octahedral complexes: The influence of the competitive effect

The reaction of cis-[RuCl(2)(P-P)(N-N)] type complexes (P-P = 1,4-bis(diphenylphosphino)butane or (1,1`-diphenylphosphino)ferrocene; N-N = 2,2`-bipyridine or 1,10-phenantroline) with monodentate ligands (L), such as 4-methylpyridine, 4-phenylpyridine and benzonitrile forms [RuCl(L)(P-P)(N-N)](+) species Upon characterization of the isolated compounds by elemental analysis, (31)P{(1)H} NMR and X-ray crystallography it was found out that the type of the L ligand determines its position in relation to the phosphorus atom. While pyridine derivatives like 4-methylpyridine and 4-phenylpyridine coordinate trans to the phosphorus atom, the benzonitrile ligand (bzCN), a good pi acceptor, coordinates trans to the nitrogen atom. A (31)P{(1)H} NMR experiment following the reaction of the precursor cis-[RuCl(2)(dppb)(phen)] with the benzonitrile ligand shows that the final position of the entering ligand in the complex is better defined as a consequence of the competitive effect between the phosphorus atom and the cyano-group from the benzonitrile moiety and not by the trans effect. In this case, the benzonitrile group is stabilized trans to one of the nitrogen atoms of the N-N ligand. A differential pulse voltammetry experiment confirms this statement. In both experiments the [RuCl(bzCN)(dppb)(phen)]PF(6) species with the bzCN ligand positioned trans to a phosphorus atom of the dppb ligand was detected as an intermediate complex. (c) 2009 Elsevier Ltd. All rights reserved.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors. (C) 2008 Elsevier B.V. All rights reserved.

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Wavelet regression with correlated errors on a piecewise Holder class

This paper generalizes the methodology of Cat and Brown [Cai, T., Brown, L.D., 1998. Wavelet shrinkage for nonequispaced samples. The Annals of Statistics 26, 1783-1799] for wavelet shrinkage for nonequispaced samples, but in the presence of correlated stationary Gaussian errors. If the true function is a member of a piecewise Holder class, it is shown that, even for long memory errors, the rate of convergence of the procedure is almost-minimax relative to the independent and identically distributed errors case. (c) 2008 Elsevier B.V. All rights reserved.

A new class of survival regression models with heavy-tailed errors: robustness and diagnostics

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.

The soft nucleophilicity of organotellurolates driving the S(N)2-type lactone ring-opening reaction

Lithium and magnesium organotellurolates were reacted with lactones producing the corresponding tellurocarboxylic acids. Treatment of the reaction mixture with lithium aluminum hydride allowed the isolation of the corresponding hydroxytellurides in a one-pot operation. (C) 2009 Published by Elsevier Ltd