By the end of 2017, articles authored by the School of Mathematics, Statistics and Actuarial Science (SMSAS) Reader in Statistics, Dr Fabrizio Leisen, Lecturer in Statistics, Dr Rachel McCrea and Emeritus, Professor Byron Morgan have been named as some of the Royal Statistical Society’s top downloaded articles in recent publication history.
The articles titled, ‘A new strategy for diagnostic model assessment in capture-recapture’ and ‘Bayesian non-parametric conditional copula estimation of twin data’ were amongst the ‘Journal of the Royal Statistical Society: Series C (Applied Statistics)’ top 20 most downloaded articles.
A new strategy for diagnostic model assessment in capture-recapture
Abstract:
Common to both diagnostic tests used in capture–recapture and score tests is the idea that starting from a simple base model it is possible to interrogate data to determine whether more complex parameter structures will be supported. Current recommendations advise that diagnostic tests are performed as a precursor to a model selection step. We show that certain well-known diagnostic tests for examining the fit of capture–recapture models to data are in fact score tests. Because of this direct relationship we investigate a new strategy for model assessment which combines the diagnosis of departure from basic model assumptions with a step-up model selection, all based on score tests. We investigate the power of such an approach to detect common reasons for lack of model fit and compare the performance of this new strategy with the existing recommendations by using simulation. We present motivating examples with real data for which the extra flexibility of score tests results in an improved performance compared with diagnostic tests.
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Bayesian non-parametric conditional copula estimation of twin data
Dalla Valle.L, Leisen.F and Rossini.L (2018) Bayesoam non-parametric conditional copula estimation of twin data, Journal of the Royal Statistical Society: Series C (Applied Statistics) 67, pp.523-548
Abstract:
Several studies on heritability in twins aim at understanding the different contribution of environmental and genetic factors to specific traits. Considering the national merit twin study, our purpose is to analyse correctly the influence of socio-economic status on the relationship between twins’ cognitive abilities. Our methodology is based on conditional copulas, which enable us to model the effect of a covariate driving the strength of dependence between the main variables. We propose a flexible Bayesian non-parametric approach for the estimation of conditional copulas, which can model any conditional copula density. Our methodology extends the work of Wu, Wang and Walker in 2015 by introducing dependence from a covariate in an infinite mixture model. Our results suggest that environmental factors are more influential in families with lower socio-economic position.
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