Evaluation and revision of inferential comprehension in narrative texts: an eye movement study

We investigated the processes of how adult readers evaluate and revise their situation model during reading by monitoring their eye movements as they read narrative texts and subsequent critical sentences. In each narrative text, a short introduction primed a knowledge-based inference, followed by a target concept that was either expected (e.g., “oven”) or unexpected (e.g., “grill”) in relation to the inferred concept. Eye movements showed that readers detected a mismatch between the new unexpected information and their prior interpretation, confirming their ability to evaluate inferential information. Just below the narrative text, a critical sentence included a target word that was either congruent (e.g., “roasted”) or incongruent (e.g., “barbecued”) with the expected but not the unexpected concept. Readers spent less time reading the congruent than the incongruent target word, reflecting the facilitation of prior information. In addition, when the unexpected (but not expected) concept had been presented, participants with lower verbal (but not visuospatial) working memory span exhibited longer reading times and made more regressions (from the critical sentence to previous information) on encountering congruent information, indicating difficulty in inhibiting their initial incorrect interpretation and revising their situation model

Sampling, WLS, and Mixed Models

Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

A compound class of Weibull and power series distributions

In this paper we introduce the Weibull power series (WPS) class of distributions which is obtained by compounding Weibull and power series distributions where the compounding procedure follows same way that was previously carried out by Adamidis and Loukas (1998) This new class of distributions has as a particular case the two-parameter exponential power series (EPS) class of distributions (Chahkandi and Gawk 2009) which contains several lifetime models such as exponential geometric (Adamidis and Loukas 1998) exponential Poisson (Kus 2007) and exponential logarithmic (Tahmasbi and Rezaei 2008) distributions The hazard function of our class can be increasing decreasing and upside down bathtub shaped among others while the hazard function of an EPS distribution is only decreasing We obtain several properties of the WPS distributions such as moments order statistics estimation by maximum likelihood and inference for a large sample Furthermore the EM algorithm is also used to determine the maximum likelihood estimates of the parameters and we discuss maximum entropy characterizations under suitable constraints Special distributions are studied in some detail Applications to two real data sets are given to show the flexibility and potentiality of the new class of distributions (C) 2010 Elsevier B V All rights reserved

On Arbitrary Selection Strategies for Basic Superposition

For first-order Horn clauses without equality, resolution is complete with an arbitrary selection of a single literal in each clause [dN 96]. Here we extend this result to the case of clauses with equality for superposition-based inference systems. Our result is a generalization of the result given in [BG 01]. We answer their question about the completeness of a superposition-based system for general clauses with an arbitrary selection strategy, provided there exists a refutation without applications of the factoring inference rule.

Evolutionary and biogeographic history of the subfamily Neoplecostominae (Siluriformes: Loricariidae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise por sequências multilocus de Xanthomonas fuscans subsp. aurantifolii

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Taking account of dependent species in management of the Southern Ocean krill fishery: estimating crabeater seal abundance off east Antarctica

1. The crabeater seal Lobodon carcinophaga is considered to be a key species in the krill-based food web of the Southern Ocean. Reliable estimates of the abundance of this species are necessary to allow the development of multispecies, predator–prey models as a basis for management of the krill fishery in the Southern Ocean. 2. A survey of crabeater seal abundance was undertaken in 1500 000 km2 of pack-ice off east Antarctica between longitudes 64–150° E during the austral summer of 1999/2000. Sighting surveys, using double observer line transect methods, were conducted from an icebreaker and two helicopters to estimate the density of seals hauled out on the ice in survey strips. Satellite-linked dive recorders were deployed on a sample of seals to estimate the probability of seals being hauled out on the ice at the times of day when sighting surveys were conducted. Model-based inference, involving fitting a density surface, was used to infer densities in the entire survey region from estimates in the surveyed areas. 3. Crabeater seal abundance was estimated to be between 0.7 and 1.4 million animals (with 95% confidence), with the most likely estimate slightly less than 1 million. 4. Synthesis and applications. The estimation of crabeater seal abundance in Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR) management areas off east Antarctic where krill biomass has also been estimated recently provides the data necessary to begin extending from single-species to multispecies management of the krill fishery. Incorporation of all major sources of uncertainty allows a precautionary interpretation of crabeater abundance and demand for krill in keeping with CCAMLR’s precautionary approach to management. While this study focuses on the crabeater seal and management of living resources in the Southern Ocean, it has also led to technical and theoretical developments in survey methodology that have widespread potential application in ecological and resource management studies, and will contribute to a more fundamental understanding of the structure and function of the Southern Ocean ecosystem.

An extended Lindley distribution

In this paper we introduce an extension of the Lindley distribution which offers a more flexible model for lifetime data. Several statistical properties of the distribution are explored, such as the density, (reversed) failure rate, (reversed) mean residual lifetime, moments, order statistics, Bonferroni and Lorenz curves. Estimation using the maximum likelihood and inference of a random sample from the distribution are investigated. A real data application illustrates the performance of the distribution. (C) 2011 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.

Computational tools for comparing asymmetric GARCH models via Bayes factors

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.

Statistical Information: A Bayesian Perspective

We explore the meaning of information about quantities of interest. Our approach is divided in two scenarios: the analysis of observations and the planning of an experiment. First, we review the Sufficiency, Conditionality and Likelihood principles and how they relate to trivial experiments. Next, we review Blackwell Sufficiency and show that sampling without replacement is Blackwell Sufficient for sampling with replacement. Finally, we unify the two scenarios presenting an extension of the relationship between Blackwell Equivalence and the Likelihood Principle.

From Amazonia to the Atlantic forest: Molecular phylogeny of Phyzelaphryninae frogs reveals unexpected diversity and a striking biogeographic pattern emphasizing conservation challenges

Documenting the Neotropical amphibian diversity has become a major challenge facing the threat of global climate change and the pace of environmental alteration. Recent molecular phylogenetic studies have revealed that the actual number of species in South American tropical forests is largely underestimated, but also that many lineages are millions of years old. The genera Phyzelaphryne (1 sp.) and Adelophryne (6 spp.), which compose the subfamily Phyzelaphryninae, include poorly documented, secretive, and minute frogs with an unusual distribution pattern that encompasses the biotic disjunction between Amazonia and the Atlantic forest. We generated >5.8 kb sequence data from six markers for all seven nominal species of the subfamily as well as for newly discovered populations in order to (1) test the monophyly of Phyzelaphryninae, Adelophryne and Phyzelaphryne, (2) estimate species diversity within the subfamily, and (3) investigate their historical biogeography and diversification. Phylogenetic reconstruction confirmed the monophyly of each group and revealed deep subdivisions within Adelophryne and Phyzelaphryne, with three major clades in Adelophryne located in northern Amazonia, northern Atlantic forest and southern Atlantic forest. Our results suggest that the actual number of species in Phyzelaphryninae is, at least, twice the currently recognized species diversity, with almost every geographically isolated population representing an anciently divergent candidate species. Such results highlight the challenges for conservation, especially in the northern Atlantic forest where it is still degraded at a fast pace. Molecular dating revealed that Phyzelaphryninae originated in Amazonia and dispersed during early Miocene to the Atlantic forest. The two Atlantic forest clades of Adelophryne started to diversify some 7 Ma minimum, while the northern Amazonian Adelophryne diversified much earlier, some 13 Ma minimum. This striking biogeographic pattern coincides with major events that have shaped the face of the South American continent, as we know it today. (C) 2012 Elsevier Inc. All rights reserved.

The chironomid-temperature relationship: expression in nature and palaeoenvironmental implications

Fossils of chironomid larvae (non-biting midges) preserved in lake sediments are well-established palaeotemperature indicators which, with the aid of numerical chironomid-based inference models (transfer functions), can provide quantitative estimates of past temperature change. This approach to temperature reconstruction relies on the strong relationship between air and lake surface water temperature and the distribution of individual chironomid taxa (species, species groups, genera) that has been observed in different climate regions (arctic, subarctic, temperate and tropical) in both the Northern and Southern hemisphere. A major complicating factor for the use of chironomids for palaeoclimate reconstruction which increases the uncertainty associated with chironomid-based temperature estimates is that the exact nature of the mechanism responsible for the strong relationship between temperature and chironomid assemblages in lakes remains uncertain. While a number of authors have provided state of the art overviews of fossil chironomid palaeoecology and the use of chironomids for temperature reconstruction, few have focused on examining the ecological basis for this approach. Here, we review the nature of the relationship between chironomids and temperature based on the available ecological evidence. After discussing many of the surveys describing the distribution of chironomid taxa in lake surface sediments in relation to temperature, we also examine evidence from laboratory and field studies exploring the effects of temperature on chironomid physiology, life cycles and behaviour. We show that, even though a direct influence of water temperature on chironomid development, growth and survival is well described, chironomid palaeoclimatology is presently faced with the paradoxical situation that the relationship between chironomid distribution and temperature seems strongest in relatively deep, thermally stratified lakes in temperate and subarctic regions in which the benthic chironomid fauna lives largely decoupled from the direct influence of air and surface water temperature. This finding suggests that indirect effects of temperature on physical and chemical characteristics of lakes play an important role in determining the distribution of lake-living chironomid larvae. However, we also demonstrate that no single indirect mechanism has been identified that can explain the strong relationship between chironomid distribution and temperature in all regions and datasets presently available. This observation contrasts with the previously published hypothesis that climatic effects on lake nutrient status and productivity may be largely responsible for the apparent correlation between chironomid assemblage distribution and temperature. We conclude our review by summarizing the implications of our findings for chironomid-based palaeoclimatology and by pointing towards further avenues of research necessary to improve our mechanistic understanding of the chironomid-temperature relationship.

A Cox Model for Biostatistics of the Future

Professor Sir David R. Cox (DRC) is widely acknowledged as among the most important scientists of the second half of the twentieth century. He inherited the mantle of statistical science from Pearson and Fisher, advanced their ideas, and translated statistical theory into practice so as to forever change the application of statistics in many fields, but especially biology and medicine. The logistic and proportional hazards models he substantially developed, are arguably among the most influential biostatistical methods in current practice. This paper looks forward over the period from DRC's 80th to 90th birthdays, to speculate about the future of biostatistics, drawing lessons from DRC's contributions along the way. We consider "Cox's model" of biostatistics, an approach to statistical science that: formulates scientific questions or quantities in terms of parameters gamma in probability models f(y; gamma) that represent in a parsimonious fashion, the underlying scientific mechanisms (Cox, 1997); partition the parameters gamma = theta, eta into a subset of interest theta and other "nuisance parameters" eta necessary to complete the probability distribution (Cox and Hinkley, 1974); develops methods of inference about the scientific quantities that depend as little as possible upon the nuisance parameters (Barndorff-Nielsen and Cox, 1989); and thinks critically about the appropriate conditional distribution on which to base infrences. We briefly review exciting biomedical and public health challenges that are capable of driving statistical developments in the next decade. We discuss the statistical models and model-based inferences central to the CM approach, contrasting them with computationally-intensive strategies for prediction and inference advocated by Breiman and others (e.g. Breiman, 2001) and to more traditional design-based methods of inference (Fisher, 1935). We discuss the hierarchical (multi-level) model as an example of the future challanges and opportunities for model-based inference. We then consider the role of conditional inference, a second key element of the CM. Recent examples from genetics are used to illustrate these ideas. Finally, the paper examines causal inference and statistical computing, two other topics we believe will be central to biostatistics research and practice in the coming decade. Throughout the paper, we attempt to indicate how DRC's work and the "Cox Model" have set a standard of excellence to which all can aspire in the future.

Fitting ACE Structural Equation Models to Case-Control Family Data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. Our likelihood-based approach to fitting involves conditioning on the proband’s disease status, as well as setting prevalence equal to a pre-specified value that can be estimated from the data themselves if necessary. Simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. These assumptions include: the usual assumptions for the classic ACE and liability-threshold models; assumptions about shared family environment for relative pairs; and assumptions about the case-control family sampling, including single ascertainment. When our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.