A new species of Cycloramphus Tschudi (Anura: Cycloramphidae) from the Parque Nacional da Serra dos Orgaos, Southeastern Brazil

We report here the discovery of a new species of frog associated to the open areas of the highlands of the Parque Nacional da Serra dos Orgaos. The new species, Cycloramphus organensis is characterized by a unique skin texture, medium size ( maximum male and female SVL 26.4 mm and 33.3 mm respectively), dorsal surfaces uniformly brick red colored, uniformly areolate skin on dorsum, pupil horizontal, iris with a menisc on upper margin; no fleshy tubercles on eyelid, tympanic annulus concealed beneath skin, macroglands not visible externally, fingers and toes without fringes and webs; supernumerary palmar and plantar tubercles absent, nuptial spines absent. Despite the presence of an iris menisc, a character shared by frogs of both genera Cycloramphus and Zachaenus Cope, the combination of morphological characters is so unique that the allocation of the species to any of these genera remains ambiguous. Consequently, we used additional molecular-based phylogenetic analyses to ascertain the position of the new taxon. The new species proved to be embedded within the genus Cycloramphus.

Infant mortality model for lifetime data

In this paper we introduce a parametric model for handling lifetime data where an early lifetime can be related to the infant-mortality failure or to the wear processes but we do not know which risk is responsible for the failure. The maximum likelihood approach and the sampling-based approach are used to get the inferences of interest. Some special cases of the proposed model are studied via Monte Carlo methods for size and power of hypothesis tests. To illustrate the proposed methodology, we introduce an example consisting of a real data set.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

NeXSPheRIO results on azimuthal anisotropy in Au-Au collisions at 200 A GeV

In this work, we present the results obtained by the hydrodynamic code NeXSPheRIO on anisotropic flows. In our calculation, we made use of event-by-event fluctuating initial conditions and chemical freeze-out was explicitly implemented. We studied directed flow, elliptic flow and forth harmonic coefficient for various hadrons at different centrality windows for Au+Au collisions at 200 A GeV. The results are discussed and compared with experimental data from RHIC.

NeXSPheRIO results on elliptic-flow fluctuations at RHIC

By using the NeXSPheRIO code, we study the elliptic-flow fluctuations in Au + Au collisions at 200 A GeV. It is shown that, by fixing the parameters of the model to correctly reproduce the charged pseudorapidity and the transverse-momentum distributions, reasonable agreement of < v(2)> with data is obtained, both as function of pseudorapidity as well as of transverse momentum, for charged particles. Our results on elliptic-flow fluctuations are in good agreement with the recently measured data on experiments.

Restoring the azimuthal symmetry of lateral distributions of charged particles in the range of the KASCADE-Grande experiment

The reconstruction of Extensive Air Showers (EAS) observed by particle detectors at the ground is based on the characteristics of observables like the lateral particle density and the arrival times. The lateral densities, inferred for different EAS components from detector data, are usually parameterised by applying various lateral distribution functions (LDFs). The LDFs are used in turn for evaluating quantities like the total number of particles or the density at particular radial distances. Typical expressions for LDFs anticipate azimuthal symmetry of the density around the shower axis. The deviations of the lateral particle density from this assumption arising from various reasons are smoothed out in the case of compact arrays like KASCADE, but not in the case of arrays like Grande, which only sample a smaller part of the azimuthal variation. KASCADE-Grande, an extension of the former KASCADE experiment, is a multi-component Extensive Air Shower (EAS) experiment located at the Karlsruhe Institute of Technology (Campus North), Germany. The lateral distributions of charged particles are deduced from the basic information provided by the Grande scintillators - the energy deposits - first in the observation plane, then in the intrinsic shower plane. In all steps azimuthal dependences should be taken into account. As the energy deposit in the scintillators is dependent on the angles of incidence of the particles, azimuthal dependences are already involved in the first step: the conversion from the energy deposits to the charged particle density. This is done by using the Lateral Energy Correction Function (LECF) that evaluates the mean energy deposited by a charged particle taking into account the contribution of other particles (e.g. photons) to the energy deposit. By using a very fast procedure for the evaluation of the energy deposited by various particles we prepared realistic LECFs depending on the angle of incidence of the shower and on the radial and azimuthal coordinates of the location of the detector. Mapping the lateral density from the observation plane onto the intrinsic shower plane does not remove the azimuthal dependences arising from geometric and attenuation effects, in particular for inclined showers. Realistic procedures for applying correction factors are developed. Specific examples of the bias due to neglecting the azimuthal asymmetries in the conversion from the energy deposit in the Grande detectors to the lateral density of charged particles in the intrinsic shower plane are given. (C) 2011 Elsevier B.V. All rights reserved.

Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

Tectonic fabric revealed by AARM of the proterozoic mafic dike swarm in the Salvador city (Bahia state): Sao Francisco Craton, NE Brazil

Magnetic fabric and rock magnetism studies were performed on 25 unmetamorphosed mafic dikes of the Meso-Late Proterozoic (similar to 1.02 Ga) dike swarm from Salvador (Bahia State, NE Brazil). This area lies in the north-eastern part of the Sao Francisco Craton, which was dominantly formed/reworked during the Transamazonian orogeny (2.14-1.94 Ga). The dikes crop out along the beaches and in quarries around Salvador city, and cut across both amphibolite dikes and granulites. Their widths range from a few centimeters up to 30 m with an average of similar to 4 m, and show two main trends N 140-190 and N 100-120 with vertical dips. Magnetic fabrics were determined using both anisotropy of low-field magnetic susceptibility (AMS) and anisotropy of anhysteretic remanent magnetization (AARM). The magnetic mineralogy was investigated by many experiments including remanent magnetization measurements at variable low temperatures (10-300 K), Mossbauer spectroscopy, high temperature magnetization curves (25-700 degrees C) and scanning electron microscopy (SEM). The rock magnetism study suggests pseudo-single-domain magnetite grains carrying the bulk magnetic susceptibility and AARM fabrics. The magnetite grains found in these dikes are large and we discard the presence of single-domain grains. Its composition is close to stoichiometric with low Ti substitution, and its Verwey transition occurs around 120 K. The main AMS fabric recognized in the swarm is so-called normal, in which the K(max)-K(int) plane is parallel to the dike plane and the magnetic foliation pole K(min)) is perpendicular to it. This fabric is interpreted as due to magma flow, and analysis of the K m inclination permitted to infer that approximately 80% of the dikes were fed by horizontal or sub-horizontal flows (K(max) < 30 degrees). This interpretation is supported by structural field evidence found in five dikes. In addition, based on the plunge of K(max), two mantle sources could be inferred; one of them which fed about 80% of the swarm would be located in the southern part of the region, and the other underlied the Valeria quarry. However, for all dikes the AARM tensors are not coaxial with AMS fabrics and show a magnetic lineation (AARM(max)) oriented to N30-60E, suggesting that magnetite grains were rotated clockwise from dike plane. The orientation of AARM lineation is similar to the orientation of a system of faults in which the Salvador normal fault is the most important. These faults were formed during Cretaceous rifting in the Reconcavo-Tucano-jatoba assemblage that corresponds to an aborted intra-continental rift formed during the opening of the South Atlantic. Therefore, the AARM fabric found for the Salvador dikes is probably tectonic in origin and suggests that the dike swarm was affected by the important tectonic event responsible for the break-up of the Gondwanaland. (C) 2008 Elsevier B.V. All rights reserved.

Inferring Contagion in Regulatory Networks

Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e., asymmetries in gene expression dependences of regulatory networks. Moreover, we present a bootstrap algorithm in order to test the contagion concept. This technique was applied in simulated data and, also, in an actual large sample of biological data. Literature review has confirmed some genes identified by contagion as actually belonging to the TP53 pathway.

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a chi (2) distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.

Improved likelihood inference in Birnbaum-Saunders regressions

The Birnbaum-Saunders regression model is commonly used in reliability studies. We address the issue of performing inference in this class of models when the number of observations is small. Our simulation results suggest that the likelihood ratio test tends to be liberal when the sample size is small. We obtain a correction factor which reduces the size distortion of the test. Also, we consider a parametric bootstrap scheme to obtain improved critical values and improved p-values for the likelihood ratio test. The numerical results show that the modified tests are more reliable in finite samples than the usual likelihood ratio test. We also present an empirical application. (C) 2009 Elsevier B.V. All rights reserved.

Modifications and Alternatives to the Tests of Levene and Brown & Forsythe for Equality of Variances and Means

The usual tests to compare variances and means (e. g. Bartlett`s test and F-test) assume that the sample comes from a normal distribution. In addition, the test for equality of means requires the assumption of homogeneity of variances. In some situation those assumptions are not satisfied, hence we may face problems like excessive size and low power. In this paper, we describe two tests, namely the Levene`s test for equality of variances, which is robust under nonnormality; and the Brown and Forsythe`s test for equality of means. We also present some modifications of the Levene`s test and Brown and Forsythe`s test, proposed by different authors. We analyzed and applied one modified form of Brown and Forsythe`s test to a real data set. This test is a robust alternative under nonnormality, heteroscedasticity and also when the data set has influential observations. The equality of variance can be well tested by Levene`s test with centering at the sample median.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Bias-corrected estimators for dispersion models with dispersion covariates

In this paper we discuss bias-corrected estimators for the regression and the dispersion parameters in an extended class of dispersion models (Jorgensen, 1997b). This class extends the regular dispersion models by letting the dispersion parameter vary throughout the observations, and contains the dispersion models as particular case. General formulae for the O(n(-1)) bias are obtained explicitly in dispersion models with dispersion covariates, which generalize previous results obtained by Botter and Cordeiro (1998), Cordeiro and McCullagh (1991), Cordeiro and Vasconcellos (1999), and Paula (1992). The practical use of the formulae is that we can derive closed-form expressions for the O(n(-1)) biases of the maximum likelihood estimators of the regression and dispersion parameters when the information matrix has a closed-form. Various expressions for the O(n(-1)) biases are given for special models. The formulae have advantages for numerical purposes because they require only a supplementary weighted linear regression. We also compare these bias-corrected estimators with two different estimators which are also bias-free to order O(n(-1)) that are based on bootstrap methods. These estimators are compared by simulation. (C) 2011 Elsevier B.V. All rights reserved.