Abstract:
In this work, we study some computational aspects for the Bayesian analysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, the use of a latent or auxiliary random variable facilitates to obtain any posterior distribution when being related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to daily price returns of Abbey National shares, considered in [1], and the other is the length distribution analysis of coding and non-coding regions in a Homo sapiens chromosome DNA sequence, considered in [2]. Posterior summaries of interest are obtained using the OpenBUGS software.

Abstract:
In this work we study the problem of constructing stochastic processes with a predetermined covariance decay by parameterizing its marginals and a given family of copulas. We present several examples to illustrate the theory, including the important Gaussian and Euclidean families of copulas. We associate the theory to common applied time series models and present a general methodology to estimate a given parameter of interest identifiable through the process' covariance decay. To exemplify the proposed methodology, we present simple Monte Carlo applications to parameter estimation in time series. The methodology is also applied to the S&P500 US stock market index.

Abstract:
In this paper we propose and study a general class of Gaussian Semiparametric Estimators (GSE) of the fractional differencing parameter in the context of long-range dependent multivariate time series. We establish large sample properties of the estimator without assuming Gaussianity. The class of models considered here satisfies simple conditions on the spectral density function, restricted to a small neighborhood of the zero frequency and includes important class of VARFIMA processes. We also present a simulation study to assess the finite sample properties of the proposed estimator based on a smoothed version of the GSE which supports its competitiveness.

Abstract:
In this paper we propose a generalization of a class of Gaussian Semiparametric Estimators (GSE) of the fractional differencing parameter for long-range dependent multivariate time series. We generalize a known GSE-type estimator by introducing some modifications at the objective function level regarding the process' spectral density matrix estimator. We study large sample properties of the estimator without assuming Gaussianity as well as hypothesis testing. The class of models considered here satisfies simple conditions on the spectral density function, restricted to a small neighborhood of the zero frequency. This includes, but is not limited to, the class of VARFIMA models. A simulation study to assess the finite sample properties of the proposed estimator is presented and supports its competitiveness. We also present an empirical application to an exchange rate data.

Abstract:
In this work we derive the copulas related to Manneville-Pomeau processes. We examine both bidimensional and multidimensional cases and derive some properties for the related copulas. Computational issues, approximations and random variate generation problems are addressed and simple numerical experiments to test the approximations developed are also performed. In particular, we propose an approximation to the copulas derived which we show to converge uniformly to the true copula. To illustrate the usefulness of the theory, we derive a fast procedure to estimate the underlying parameter in Manneville-Pomeau processes.

Abstract:
We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We present a simulated study regarding the estimation of the risk measure $VaR_p$ on FIEGARCH processes. We consider the distribution function of the portfolio log-returns (univariate case) and the multivariate distribution function of the risk-factor changes (multivariate case). We also compare the performance of the risk measures $VaR_p$, $ES_p$ and MaxLoss for a portfolio composed by stocks of four Brazilian companies.

Abstract:
Here we present a theoretical study on the main properties of Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic (FIEGARCH) processes. We analyze the conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We prove that, if $\{X_t\}_{t \in \mathds{Z}}$ is a FIEGARCH$(p,d,q)$ process then, under mild conditions, $\{\ln(X_t^2)\}_{t\in\mathds{Z}}$ is an ARFIMA$(q,d,0)$, that is, an autoregressive fractionally integrated moving average process. The convergence order for the polynomial coefficients that describes the volatility is presented and results related to the spectral representation and to the covariance structure of both processes $\{\ln(X_t^2)\}_{t\in\mathds{Z}}$ and $\ {\ln(\sigma_t^2)\}_{t\in\mathds{Z}}$ are also discussed. Expressions for the kurtosis and the asymmetry measures for any stationary FIEGARCH$(p,d,q)$ process are also derived. The $h$-step ahead forecast for the processes $\{X_t\}_{t \in \mathds{Z}}$, $\{\ln(\sigma_t^2)\}_{t\in\mathds{Z}}$ and $\{\ln(X_t^2)\}_{t\in\mathds{Z}}$ are given with their respective mean square error forecast. The work also presents a Monte Carlo simulation study showing how to generate, estimate and forecast based on six different FIEGARCH models. The forecasting performance of six models belonging to the class of autoregressive conditional heteroskedastic models (namely, ARCH-type models) and radial basis models is compared through an empirical application to Brazilian stock market exchange index.

Abstract:
Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroskedastic (FIEGARCH) models using Markov Chain Monte Carlo (MCMC) methods is described. A simulation study is presented to access the performance of the procedure, under the presence of long-memory in the volatility. Samples from FIEGARCH processes are obtained upon considering the generalized error distribution (GED) for the innovation process. Different values for the tail-thickness parameter \nu are considered covering both scenarios, innovation processes with lighter (\nu<2) and heavier (\nu>2) tails than the Gaussian distribution (\nu=2). A sensitivity analysis is performed by considering different prior density functions and by integrating (or not) the knowledge on the true parameter values to select the hyperparameter values.

The central importance of quantum chemistry is to obtain solutions of the Schr?dinger equation for the accurate determination of the properties of atomic and molecular systems that occurred from the calculation of wave functions accurate for many diatomic and polyatomic molecules, using Self Consistent Field method (SCF). The application of quantum chemical methods in the study and planning of bioactive compounds has become a common practice nowadays. From the point of view of planning it is important to note, when it comes to the use of molecular modeling, a collective term that refers to methods and theoretical modeling and computational techniques to mimic the behavior of molecules, not intend to reach a bioactive molecule simply through the use of computer programs. The choice of method for energy minimization depends on factors related to the size of the molecule, parameters of availability, stored data and computational resources. Molecular models generated by the computer are the result of mathematical equations that estimate the positions and properties of the electrons and nuclei, the calculations exploit experimentally, the characteristics of a structure, providing a new perspective on the molecule. In this work we show that studies of Highest Occupied Molecular Orbital Energy (HOMO), Low Unoccupied Molecular Orbital Energy (LUMO) and Map of molecular electrostatic potential (MEP) using Hatree-Fock method with different basis sets (HF/3-21G*, HF/3-21G**, HF/6-31G, HF/6-31G*, HF/6-31G** and HF/6-311G), that are of great importance in modern chemistry, biochemistry, molecular biology, and other fields of knowledge of health sciences. In order to obtain a significant correlation, it is essential that the descriptors are used appropriately. Thus, the quantum chemical calculations are an attractive source of new molecular descriptors that can, in principle, express all the geometrical and electronic properties of molecules and their interactions with biological receptor.

Abstract:
... E o Brasil ficou sendo o que é liricamente. E o Brasil ficou tendo a forma de uma harpa geograficamente. E o Brasil é este poema menino que acontece na vida da gente... Cassiano RicardoEste artigo é produto de um trabalho maior, apresentado na Faculdade de Filosofia, Letras e Ciências Humanas da Universidade de S o Paulo, para a conclus o do curso de gradua o em geografia. Com o título A Gesta Bandeirante – O Regionalismo Paulista na Obra de Cassiano Ricardo, o trabalho consistiu num est...