The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes)

Conservation biology aims at assessing the status of a population, based on information which is often incomplete. Integrated population modelling based on state‐space models appears to be a powerful and relevant way of combining into a single likelihood several types of information such as capture‐recapture data and population surveys. In this paper, the authors describe the principles of integrated population modelling and they evaluate its performance for conservation biology based on a case study, that of the black‐footed albatross, a northern Pacific albatross species suspected to be impacted by longline fishing     

Erroneous perceptions and arousal among regular and occasional video poker players

This study investigated the relationship between the level of arousal (Heart Rate) and the number of erroneous perceptions among video poker players. Twelve regular and twelve occasional gamblers participated in a gambling session conducted in a natural environment. It was hypothesised that 1) a significant positive correlation would be observed between arousal and the number of erroneous verbalizations, 2) regular players would show a higher level of arousal than occasional players, and 3) they would emit more erroneous verbalizations. Results showed that hypotheses one and three were confirmed. Theoretical and practical implications of these results for the psychology of gambling are discussed.This study was supported by a grant from Loto-Québec.     

Demographic Shifts in the Czech Republic after 1989: A Second Demographic Transition View

A dramatic change in fertility,family formation and living arrangements tookplace in the Czech Republic over the 1990s. Theestablishment of democracy, profound socialtransformation and transition to the marketeconomy affected the values and demographicbehaviour of the young Czech generation. Thispaper examines whether these demographic shiftscan be interpreted within the framework of thesecond demographic transition. The theoreticalpart discusses the idea of the transition,outlining three distinctive conceptualisations.Two of them – the view of the transition as aprogression of characteristic interrelateddemographic changes and a broader viewstressing the importance of underlyingideational factors – fit the Czech situationvery well. A comparison with the Netherlandsreveals that the onset of the transition in theCzech Republic may be clearly located in thefirst half of the 1990s, lagging two decadesbehind the Netherlands.     

Gendered Perspectives in Psychology: Immigrant Origin Youth

In this article, we contend that the field of psychology has largely failed to foreground the role of gender in its study of immigration. Here, we review studies that address gender and migration focusing on the experience of children and adolescents. We provide developmental perspectives on family relations, well‐being, identity formation, and educational outcomes, paying particular attention to the role of gender in these domains. We conclude with recommendations for future research, which include the need to consider whether, and if so, how, when, and why it makes a difference to be an immigrant, to be from a particular country, or to be female rather than male. We argue that it is important to consider socioeconomic characteristics; to consider resilience as well as pathology; and to work in interdisciplinary ways to deepen our understanding of the gendered migratory experience of immigrant origin youth.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.     

Nonparametric estimation of the conditional distribution function in a semiparametric censorship model

In this paper we propose a new nonparametric estimator of the conditional distribution function under a semiparametric censorship model. We establish an asymptotic representation of the estimator as a sum of iid random variables, balanced by some kernel weights. This representation is used for obtaining large sample results such as the rate of uniform convergence of the estimator, or its limit distributional law. We prove that the new estimator outperforms the conditional Kaplan–Meier estimator for censored data, in the sense that it exhibits lower asymptotic variance. Illustration through real data analysis is provided.     

Improved model selection criteria for SETAR time series models

The purpose of this paper is threefold. First, we obtain the asymptotic properties of the modified model selection criteria proposed by Hurvich et al. (1990. Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709–719) for autoregressive models. Second, we provide some highlights on the better performance of this modified criteria. Third, we extend the modification introduced by these authors to model selection criteria commonly used in the class of self-exciting threshold autoregressive (SETAR) time series models. We show the improvements of the modified criteria in their finite sample performance. In particular, for small and medium sample size the frequency of selecting the true model improves for the consistent criteria and the root mean square error (RMSE) of prediction improves for the efficient criteria. These results are illustrated via simulation with SETAR models in which we assume that the threshold and the parameters are unknown.     

Some results on optimality in models with heteroscedastic errors from partial optimum designs

This paper studies optimum designs for linear models when the errors are heteroscedastic. Sufficient conditions are given in order to obtainD-, A- andE-optimum designs for a complete regression model from partial optimum designs for some sub-parameters. A result about optimality for a complete model from the optimality for the submodels is included. Supported by Junta de Andalucía, research group FQM244.