Répteis da Estação Ecológica Serra Geral do Tocantins, Brasil Central

Os Cerrados sul-americanos abrigam alta diversidade de répteis, incluindo elevado número de endemismos. No entanto, o conhecimento desta diversidade é ainda incompleto frente à acelerada transformação das paisagens naturais no Brasil central. Constituem, portanto, uma das regiões prioritárias para estudo e conservação da biodiversidade mundial. Estudos intensivos sobre a fauna de répteis do Cerrado são necessários e urgentes para melhor compreensão dos processos que levaram à sua origem e distribuição e para subsidiar ações de conservação. Por meio de métodos padronizados, amostramos duas regiões ainda inexploradas da Estação Ecológica Serra Geral do Tocantins, situada na região do Jalapão. Registramos 45 espécies de répteis para a EESGT e entorno, o que representa uma riqueza alta e comparável à de outras regiões bem amostradas do Cerrado. Curvas de acumulação e estimadores indicam que a riqueza local de lagartos e anfisbenídeos aproxima-se da riqueza real enquanto a de serpentes é subestimada. A distribuição não-aleatória das espécies na paisagem concorda com evidências anteriores sugerindo utilização diferencial dos hábitats pelos répteis. Reunindo os resultados do presente estudo com os de levantamentos prévios realizados na região, registramos 88 espécies de répteis para o Jalapão sendo oito registros novos que incluem Bachia oxyrhina uma espécie recém descrita da região. As espécies da área apresentam três padrões gerais de distribuição: (1) espécies endêmicas do Cerrado, (2) espécies compartilhadas com domínios da diagonal de formações abertas sul-americanas, e (3) espécies de ampla ocorrência, compartilhadas também com ecossistemas florestais. Prevalecem espécies de ampla distribuição, porém é grande o número de espécies típicas do Cerrado, incluindo cinco possivelmente endêmicas do Jalapão, e há contribuição importante da fauna da Caatinga. A distribuição dos répteis em escala local e regional demonstra a necessidade de considerar a heterogeneidade paisagística para o planejamento de diretrizes visando à conservação em regiões do Cerrado. Por sua grande extensão, posição biogeográfica e complexidade de relevo e tipos de hábitat, a EESGT tem papel fundamental para a preservação e conhecimento da diversidade de répteis do Cerrado.

Técnicas de diagnóstico de influência local na análise espacial da produtividade da soja

A modelagem da estrutura de dependência espacial pela abordagem da geoestatística é fundamental para a definição de parâmetros que definem esta estrutura, e que são utilizados na interpolação de valores em locais não amostrados pela técnica de krigagem. Entretanto, a estimação de parâmetros pode ser muito afetada pela presença de observações atípicas nos dados amostrados. O desenvolvimento deste trabalho teve por objetivo utilizar técnicas de diagnóstico de influência local em modelos espaciais lineares gaussianos, utilizados em geoestatística, para avaliar a sensibilidade dos estimadores de máxima verossimilhança e máxima verossimilhança restrita na presença de dados discrepantes. Estudos com dados experimentais mostraram que tanto a presença de valores atípicos como de valores considerados influentes, pela análise de diagnóstico, pode exercer forte influência nos mapas temáticos, alterando, assim, a estrutura de dependência espacial. As aplicações de técnicas de diagnóstico de influência local devem fazer parte de toda análise geoestatística a fim de garantir que as informações contidas nos mapas temáticos tenham maior qualidade e possam ser utilizadas com maior segurança pelo agricultor.

Shared information in stationary states of stochastic processes

We present four estimators of the shared information (or interdepency) in ground states given that the coefficients appearing in the wave function are all real non-negative numbers and therefore can be interpreted as probabilities of configurations. Such ground states of Hermitian and non-Hermitian Hamiltonians can be given, for example, by superpositions of valence bond states which can describe equilibrium but also stationary states of stochastic models. We consider in detail the last case, the system being a classical not a quantum one. Using analytical and numerical methods we compare the values of the estimators in the directed polymer and the raise and peel models which have massive, conformal invariant and nonconformal invariant massless phases. We show that like in the case of the quantum problem, the estimators verify the area law with logarithmic corrections when phase transitions take place.

INFERENCE FOR EIGENVALUES AND EIGENVECTORS OF GAUSSIAN SYMMETRIC MATRICES

This article presents maximum likelihood estimators (MLEs) and log-likelihood ratio (LLR) tests for the eigenvalues and eigenvectors of Gaussian random symmetric matrices of arbitrary dimension, where the observations are independent repeated samples from one or two populations. These inference problems are relevant in the analysis of diffusion tensor imaging data and polarized cosmic background radiation data, where the observations are, respectively, 3 x 3 and 2 x 2 symmetric positive definite matrices. The parameter sets involved in the inference problems for eigenvalues and eigenvectors are subsets of Euclidean space that are either affine subspaces, embedded submanifolds that are invariant under orthogonal transformations or polyhedral convex cones. We show that for a class of sets that includes the ones considered in this paper, the MLEs of the mean parameter do not depend on the covariance parameters if and only if the covariance structure is orthogonally invariant. Closed-form expressions for the MLEs and the associated LLRs are derived for this covariance structure.

An oracle approach for interaction neighborhood estimation in random fields

We consider the problem of interaction neighborhood estimation from the partial observation of a finite number of realizations of a random field. We introduce a model selection rule to choose estimators of conditional probabilities among natural candidates. Our main result is an oracle inequality satisfied by the resulting estimator. We use then this selection rule in a two-step procedure to evaluate the interacting neighborhoods. The selection rule selects a small prior set of possible interacting points and a cutting step remove from this prior set the irrelevant points. We also prove that the Ising models satisfy the assumptions of the main theorems, without restrictions on the temperature, on the structure of the interacting graph or on the range of the interactions. It provides therefore a large class of applications for our results. We give a computationally efficient procedure in these models. We finally show the practical efficiency of our approach in a simulation study.

Extensions of discrete triangular distributions and boundary bias in kernel estimation for discrete functions

Asymmetric discrete triangular distributions are introduced in order to extend the symmetric ones serving for discrete associated kernels in the nonparametric estimation for discrete functions. The extension from one to two orders around the mode provides a large family of discrete distributions having a finite support. Establishing a bridge between Dirac and discrete uniform distributions, some different shapes are also obtained and their properties are investigated. In particular, the mean and variance are pointed out. Applications to discrete kernel estimators are given with a solution to a boundary bias problem. (C) 2010 Elsevier B.V. All rights reserved.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is 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 for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

On modifications to the long-term survival mixture model in the presence of competing risks

A mixture model for long-term survivors has been adopted in various fields such as biostatistics and criminology where some individuals may never experience the type of failure under study. It is directly applicable in situations where the only information available from follow-up on individuals who will never experience this type of failure is in the form of censored observations. In this paper, we consider a modification to the model so that it still applies in the case where during the follow-up period it becomes known that an individual will never experience failure from the cause of interest. Unless a model allows for this additional information, a consistent survival analysis will not be obtained. A partial maximum likelihood (ML) approach is proposed that preserves the simplicity of the long-term survival mixture model and provides consistent estimators of the quantities of interest. Some simulation experiments are performed to assess the efficiency of the partial ML approach relative to the full ML approach for survival in the presence of competing risks.

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting. Copyright (C) 2004 John Wiley Sons, Ltd.

High-breakdown linear discriminant analysis

The classification rules of linear discriminant analysis are defined by the true mean vectors and the common covariance matrix of the populations from which the data come. Because these true parameters are generally unknown, they are commonly estimated by the sample mean vector and covariance matrix of the data in a training sample randomly drawn from each population. However, these sample statistics are notoriously susceptible to contamination by outliers, a problem compounded by the fact that the outliers may be invisible to conventional diagnostics. High-breakdown estimation is a procedure designed to remove this cause for concern by producing estimates that are immune to serious distortion by a minority of outliers, regardless of their severity. In this article we motivate and develop a high-breakdown criterion for linear discriminant analysis and give an algorithm for its implementation. The procedure is intended to supplement rather than replace the usual sample-moment methodology of discriminant analysis either by providing indications that the dataset is not seriously affected by outliers (supporting the usual analysis) or by identifying apparently aberrant points and giving resistant estimators that are not affected by them.

Association between micronuclei frequency in pollen mother cells of Tradescantia and mortality due to cancer and cardiovascular diseases: A preliminary study in Sao Jose dos Campos, Brazil

The present study was designed to explore the correlation between the frequency of micronuclei in Trad-MN, measured across 28 biomonitoring stations during the period comprised between 11 of May and 2 of October, 2006, and adjusted mortality rates due to cardiovascular, respiratory diseases and cancer in Sao Jose dos Campos, Brazil, an area with different sources of air pollution. For controlling purposes, mortality rate due to gastrointestinal diseases (an event less prone to be affected by air pollution) was also considered in the analysis. Spatial distribution of micronuclei frequency was determined using average interpolation. The association between health estimators and micronuclei frequency was determined by measures of Pearson`s correlation. Higher frequencies of micronuclei were detected in areas with high traffic and close to a petrochemical pole. Significant associations were detected between micronuclei frequency and adjusted mortality rate due to cardiovascular diseases (r = 0.841, p = 0.036) and cancer (r = 0.890, p = 0.018). The association between mortality due to chronic obstructive pulmonary diseases was positive but did not reach statistical significance (r = 0.640, p = 0.172), probably because of the small number of events. Gastrointestinal mortality did not exhibit significant association with micronuclei frequency. Because the small number of observations and the nature of an ecological study, the present findings must be considered with caution and considered as preliminary. Further studies, performed in different conditions of contamination and climate should be done before considering Trad-MN in the evaluation of human health risk imposed by air pollutants. (C) 2009 Elsevier Ltd. All rights reserved.

A Comparison of the Home Range Sizes of Mainland and Island Populations of Black-Faced Lion Tamarins (Leontopithecus caissara) Using Different Spatial Analysis

The critically endangered black-faced lion tamarin, Leontopithecus caissara, has a restricted geographical distribution consisting of small mainland and island populations, each with distinct habitats in coastal southeastern Brazil. Necessary conservation management actions require an assessment of whether differences in habitats are reflected in use of space by the species. We studied two tamarin groups on the mainland at Sao Paulo state between August 2005 and March 2007, and compared the results with data from Superagui Island. Three home range estimators were used: minimum convex polygon (MCP), Kernel, and the new technique presented dissolved monthly polygons (DMP). These resulted, respectively, in home ranges of 345, 297, and 282 ha for the 12-month duration of the study. Spatial overlap of mainland groups was extensive, whereas temporal overlap was not, a pattern that indicates resource partitioning is an important strategy to avoid intraspecific competition. L. caissara large home ranges seem to be dynamic, with constant incorporation of new areas and abandonment of others through time. The main difference between mainland and island groups is the amount and variety of sleeping sites. A better understanding of the home range sizes, day range lengths, and territorial behavior of this species will aid in developing better management strategies for its protection. Additionally, the presented DMP protocol is a useful improvement over the MCP method as it results in more realistic home range sizes for wildlife species. Am. J. Primatol. 73: 1114-1126, 2011. (C) 2011 Wiley Periodicals, Inc.

Model specification tests in nonparametric stochastic regression models

In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established. We also conduct a small sample study for one of the test statistics through a simulated example. (C) 2002 Elsevier Science (USA).