Patterns of diversification in two species of short-tailed bats (Carollia Gray, 1838): the effects of historical fragmentation of Brazilian rainforests

The small-sized frugivorous bat Carollia perspicillata is an understory specialist and occurs in a wide range of lowland habitats, tending to be more common in tropical dry or moist forests of South and Central America. Its sister species, Carollia brevicauda, occurs almost exclusively in the Amazon rainforest. A recent phylogeographic study proposed a hypothesis of origin and subsequent diversification for C. perspicillata along the Atlantic coastal forest of Brazil. Additionally, it also found two allopatric clades for C. brevicauda separated by the Amazon Basin. We used cytochrome b gene sequences and a more extensive sampling to test hypotheses related to the origin and diversification of C. perspicillata plus C. brevicauda clade in South America. The results obtained indicate that there are two sympatric evolutionary lineages within each species. In C. perspicillata, one lineage is limited to the Southern Atlantic Forest, whereas the other is widely distributed. Coalescent analysis points to a simultaneous origin for C. perspicillata and C. brevicauda, although no place for the diversification of each species can be firmly suggested. The phylogeographic pattern shown by C. perspicillata is also congruent with the Pleistocene refugia hypothesis as a likely vicariant phenomenon shaping the present distribution of its intraspecific lineages. (C) 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 102, 527-539.

On estimation and influence diagnostics for the Grubbs` model under heavy-tailed distributions

The Grubbs` measurement model is frequently used to compare several measuring devices. It is common to assume that the random terms have a normal distribution. However, such assumption makes the inference vulnerable to outlying observations, whereas scale mixtures of normal distributions have been an interesting alternative to produce robust estimates, keeping the elegancy and simplicity of the maximum likelihood theory. The aim of this paper is to develop an EM-type algorithm for the parameter estimation, and to use the local influence method to assess the robustness aspects of these parameter estimates under some usual perturbation schemes, In order to identify outliers and to criticize the model building we use the local influence procedure in a Study to compare the precision of several thermocouples. (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.