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Summary. We analyse the education–fertility relationship by using data on women from Botswana. A realistic quantification of such a relationship can be problematic for various reasons. First, factors such as motivation and ability are associated with fertility and education but cannot be observed and as a consequence cannot be included in the model. Here, the use of classical estimation methods will...
Summary. In the course of national sports tournaments, usually lasting several months, it is expected that the abilities of teams taking part in the tournament will change over time. A dynamic extension of the Bradley–Terry model for paired comparison data is introduced to model the outcomes of sporting contests, allowing for time varying abilities. It is assumed that teams’ home and away abilities...
Summary. This work is concerned with the vulnerability of spaceborne microelectronics to single‐event upset, which is a change of state caused by high‐energy charged particles in the solar wind or the cosmic ray environment striking a sensitive node. To measure the susceptibility of a semiconductor device to single‐event upsets, testing is conducted by exposing it to high‐energy heavy ions or protons...
Summary. A common conjecture in the study of publication bias is that studies reporting a significant result are more likely to be selected for review than studies whose results are inconclusive. We envisage a population of studies following the standard random‐effects model of meta‐analysis, and a selection probability given by a function of the study's ‘t‐statistic’. In practice it is difficult...
Summary. Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather‐related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain...
Summary. Data are often collected from wild animals that have been marked at unknown age. As a result, standard probability models, fitted by maximum likelihood, cannot incorporate age dependence in probabilities of annual survival. We propose and fit new mixture models to ring–recovery data on birds ringed of unknown age, in which it is possible to incorporate age dependence in survival. It is shown...
Summary. Typical oncology practice often includes not only an initial front‐line treatment but also subsequent treatments given if the initial treatment fails. The physician chooses a treatment at each stage based on the patient's baseline covariates and history of previous treatments and outcomes. Such sequentially adaptive medical decision‐making processes are known as dynamic treatment regimes,...
Summary. The dose finding problem for single compounds has been generalized to combination therapies and several binary regression models have been proposed in the literature. We propose general tools to evaluate them, in particular dose‐free parameterizations of the risks of toxicity and risk ratio functions and plots. New classes of risk functions are also proposed. They generate low dimensional...
Summary. The paper addresses the optimal choice of the waiting period (or time out) that a device should respect before entering sleep mode, to optimize a trade‐off between power consumption and device availability. The optimal time out is inferred by appropriate statistical modelling of the times between user requests. In our first approach, these times are assumed to be independent, and a constant...
Summary. Motivated by a practical application, the paper investigates robust estimation of economic indicators from survey samples based on a semiparametric Pareto tail model. Economic performance is typically measured by a set of indicators, which are often estimated from survey data—the motivating example being the European indicators on social exclusion and poverty computed from the well‐known...
Summary. The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is relatively small and/or certain outcome categories are rare, maximum likelihood can yield biased estimates of the regression parameters. Firth and Kosmidis proposed a procedure to remove the leading...
Summary. The paper is devoted to the development of a statistical framework for air quality assessment at the country level and for the evaluation of the ambient population exposure and risk with respect to airborne pollutants. The framework is based on a multivariate space–time model and on aggregated indices defined at different levels of aggregation in space and time. The indices are evaluated,...
Summary. Auxiliary variables that are associated with both key survey variables and response propensity are important for post‐survey non‐response adjustments, but rare. Interviewer observations on sample units and linked auxiliary variables from commercially available household databases are promising candidates, but these variables are prone to error. The assumption of missingness at random that...
Summary. Linear multiregression dynamic models, which combine a graphical representation of a multivariate time series with a state space model, have been shown to be a promising class of models for forecasting traffic flow data. Analysis of flows at a busy motorway intersection near Manchester, UK, highlights two important modelling issues: accommodating different levels of traffic variability depending...
Summary. To prevent accidents caused by unexploded bombs from the Second World War, high‐risk zones need to be determined. We introduce two statistical methods to determine such zones by considering patterns of exploded and unexploded bombs as realizations of inhomogeneous spatial Poisson point processes. The first method is based on the intensity of the point process; the second method on its nearest...
Summary. The paper describes a complex multistate modelling approach for the analysis of association between the onset of diabetes and the diagnosis of pancreatic cancer among families with hereditary pancreatitis. The model allows for competing risks, correlated survival times and time‐dependent covariates, so taking several interrelated factors into account: the consequences of pancreatic resection,...
Summary. The paper describes a Bayesian spatial discrete time survival model to estimate the effect of air pollution on the risk of preterm birth. The standard approach treats prematurity as a binary outcome and cannot effectively examine time varying exposures during pregnancy. Time varying exposures can arise either in short‐term lagged exposures due to seasonality in air pollution or long‐term...
Summary. Data with mixed‐type (metric–ordinal–nominal) variables are typical for social stratification, i.e. partitioning a population into social classes. Approaches to cluster such data are compared, namely a latent class mixture model assuming local independence and dissimilarity‐based methods such as k‐medoids. The design of an appropriate dissimilarity measure and the estimation of the number...
Summary. In a unique longitudinal study of teen driving, risky driving behaviour and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and developing a predictor of crashes from previous risky driving behaviour. In this work, we propose two latent class models for relating risky driving...
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