Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation

We consider mark–recapture–recovery data with additional individual time-varying continuous covariate data. For such data it is common to specify the model parameters, and in particular the survival probabilities, as a function of these covariates to incorporate individual heterogeneity. However, an issue arises in relation to missing covariate values, for (at least) the times when an individual is not observed, leading to an analytically intractable likelihood. We propose a two-step multiple imputation approach to obtain estimates of the demographic parameters. Firstly, a model is fitted to only the observed covariate values. Conditional on the fitted covariate model, multiple “complete” datasets are generated (i.e. all missing covariate values are imputed). Secondly, for each complete dataset, a closed form complete data likelihood can be maximised to obtain estimates of the model parameters which are subsequently combined to obtain an overall estimate of the parameters. Associated standard errors and 95 % confidence intervals are obtained using a non-parametric bootstrap. A simulation study is undertaken to assess the performance of the proposed two-step approach. We apply the method to data collected on a well-studied population of Soay sheep and compare the results with a Bayesian data augmentation approach. Supplementary materials accompanying this paper appear on-line.Publisher PDFPeer reviewe

Pairwise interaction point processes for modelling bivariate spatial point patterns in the presence of interaction uncertainty

Current ecological research seeks to understand the mechanisms that sustain biodiversity and allow a large number of species to coexist. Coexistence concerns inter-individual interactions. Consequently, there is an interest in identifying and quantifying interactions within and between species as reflected in the spatial pattern formed by the individuals. This study analyses the spatial pattern formed by the locations of plants in a community with high biodiversity from Western Australia. We fit a pairwise interaction Gibbs marked point process to the data using a Bayesian approach and quantify the inhibitory interactions within and between the two species. We quantitatively discriminate between competing models corresponding to different inter-specific and intraspecific interactions via posterior model probabilities. The analysis provides evidence that the intraspecific interactions for the two species of the genus Banksia are generally similar to those between the two species providing some evidence for mechanisms that sustain biodiversity.Publisher PDFPeer reviewe

Importance Tempering

Simulated tempering (ST) is an established Markov chain Monte Carlo (MCMC) method for sampling from a multimodal density $\pi(\theta)$. Typically, ST involves introducing an auxiliary variable $k$ taking values in a finite subset of $[0,1]$ and indexing a set of tempered distributions, say $\pi_k(\theta) \propto \pi(\theta)^k$. In this case, small values of $k$ encourage better mixing, but samples from $\pi$ are only obtained when the joint chain for $(\theta,k)$ reaches $k=1$. However, the entire chain can be used to estimate expectations under $\pi$ of functions of interest, provided that importance sampling (IS) weights are calculated. Unfortunately this method, which we call importance tempering (IT), can disappoint. This is partly because the most immediately obvious implementation is na\"ive and can lead to high variance estimators. We derive a new optimal method for combining multiple IS estimators and prove that the resulting estimator has a highly desirable property related to the notion of effective sample size. We briefly report on the success of the optimal combination in two modelling scenarios requiring reversible-jump MCMC, where the na\"ive approach fails.Comment: 16 pages, 2 tables, significantly shortened from version 4 in response to referee comments, to appear in Statistics and Computin

Acadian French and Linguistic Theory

This article identifies some profitable areas of research for Acadian linguistics, in particular, research in sociolinguistics and in grammatical theory. The value of Acadian French data for the testing of theories of language change and for the study of syntactic variation within generative grammar is explored

Back to Back: The Trajectory of an Old Borrowing

In this paper I explore changes in varieties of North American French resulting from a) loss in productivity of the re- prefix in French; and b) availability of the English particle back in contact varieties. We see that addition of back to speakers’ repertoires is dependent upon social factors while its integration into the grammar of vernacular varieties may involve both semantic and syntactic reanalysis of the English lexical item. Under the right social conditions, back becomes a French aspectual adverb

'Quite' in Acadian French : a variety of Scalar Focus

This paper discusses the properties of the 'quite de' construction in the Baie Sainte-Marie, Nova Scotia variety of Acadian French in examples such as le/un quite de souper 'quite the/a supper' which have a hyperbolic connotation. Unlike epithet nouns and other binominals of French (and Romance more generally), we argue that this Acadian French construction contains a complex DP with a unique N but an expanded left nominal periphery (cf. Giusti 2005, 2006, 2012). In order to unify its syntactic and semantic properties, we propose an analysis where [Adv quite] is a scalar Focus head (following Kayne's 1998 treatment of scalar only), with a null M(easurement) Op(erator) (Rett 2008, 2011) in its specifier. This M-op is associated with a degree argument (i.e. N) bound by existential closure, which forces its linearization within the predicate domain (cf. Diesing 1992). Lastly, we show that de in the AF 'quite de' construction cannot be construed as a linker (pace den Dikken 2004, 2006) or as a P category (pace Kayne 1994), but is best analysed as a nominal functor (cf. Ihsane 2013) at the Comp-Infl interface whose role is to recategorize a referential DP into a property denoting DP

The role of anterior cingulate cortex in the affective evaluation of conflict

An influential theory of anterior cingulate cortex (ACC) function argues that this brain region plays a crucial role in the affective evaluation of performance monitoring and control demands. Specifically, control-demanding processes such as response conflict are thought to be registered as aversive signals by ACC, which in turn triggers processing adjustments to support avoidance learning. In support of conflict being treated as an aversive event, recent behavioral studies demonstrated that incongruent (i.e., conflict inducing), relative to congruent, stimuli can speed up subsequent negative, relative to positive, affective picture processing. Here, we used fMRI to investigate directly whether ACC activity in response to negative versus positive pictures is modulated by preceding control demands, consisting of conflict and task-switching conditions. The results show that negative, relative to positive, pictures elicited higher ACC activation after congruent, relative to incongruent, trials, suggesting that ACC's response to negative (positive) pictures was indeed affectively primed by incongruent (congruent) trials. Interestingly, this pattern of results was observed on task repetitions but disappeared on task alternations. This study supports the proposal that conflict induces negative affect and is the first to show that this affective signal is reflected in ACC activation