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Both dropout and death can truncate observation of a longitudinal outcome. Since extrapolation beyond death is often not appropriate, it is desirable to obtain the longitudinal outcome profile of a population given being alive. We propose a new likelihood‐based approach to accommodating informative dropout and death by jointly modelling the longitudinal outcome and semicompeting event times of dropout...
Recent advances in molecular biology allow the quantification of the transcriptome and scoring transcripts as differentially or equally expressed between two biological conditions. Although these two tasks are closely linked, the available inference methods treat them separately: a primary model is used to estimate expression and its output is post processed by using a differential expression model...
Osteoporotic fractures are known to be highly recurring. We investigate bone‐dependent and bone‐independent risk factors of osteoporotic fracture frequency and relative proportions at various body locations by using the data from the osteoporotic fracture study that was conducted by the National Health and Nutrition Examination Survey, 2007–2008. We propose a new zero‐inflated baseline category multinomial...
Bat echolocation call identification methods are important in developing efficient cost‐effective methods for large‐scale bioacoustic surveys for global biodiversity monitoring and conservation planning. Such methods need to provide interpretable probabilistic predictions of species since they will be applied across many different taxa in a diverse set of applications and environments. We develop...
Although death rates from heart disease have declined sharply over the past 50 years, the rate of decline varies by location, race and sex. Despite these declines, heart disease continues to be the leading cause of death in the USA. We propose a non‐separable multivariate spatiotemporal Bayesian model to obtain a clearer picture of the temporally varying trends in county level heart disease death...
In many applications, process monitoring has to deal with functional responses, which are also known as profile data. In these scenarios, a relevant industrial problem consists of detecting faults by combining supervised learning with functional data analysis and statistical process monitoring. Supervised learning is usually applied to the whole signal domain, with the aim of discovering the features...
In response to the 2015 Royal Statistical Society's statistical analytics challenge, we propose to model the fixation locations of the human eye when observing a still image by a Markov point process in . Our approach is data driven using k‐means clustering of the fixation locations to identify distinct salient regions of the image, which in turn correspond to the states of our Markov chain. Bayes...
When comparing time varying treatments in a non‐randomized setting, one must often correct for time‐dependent confounders that influence treatment choice over time and that are themselves influenced by treatment. We present a new two‐step procedure, based on additive hazard regression and linear increments models, for handling such confounding when estimating average treatment effects on the treated...
The incomplete informative cluster size problem is motivated by the National Institute of Child Health and Human Development consecutive pregnancies study, aiming to study the relationship between pregnancy outcomes and parity. These pregnancy outcomes are potentially associated with the number of births over a woman's lifetime, resulting in an incomplete informative cluster size (censored at the...
We introduce generalized Chao and generalized Zelterman estimators which include individual, time varying and behavioural effects. Under mild assumptions in the presence of unobserved heterogeneity, the generalized Chao estimator asymptotically provides a lower bound for the population size and is unbiased otherwise. Corrected versions guarantee bounded estimates. To include the best set of predictors...
Extreme heat, or persistently high temperatures in the form of heatwaves, adversely impacts human health. To study such effects, risk maps are a common epidemiological tool that is used to identify regions and populations that are more susceptible to these negative outcomes; however, the negative health effects of high temperatures are manifested differently between different segments of the population...
Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM2.5). The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks but, although coverage is increasing, regions remain in which monitoring is limited. The data integration...
Many randomized controlled trials report more than one primary outcome. As a result, multivariate meta‐analytic methods for the assimilation of treatment effects in systematic reviews of randomized controlled trials have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared with univariate meta‐analysis. However, most...
A prevailing viewpoint in paleoclimate science is that a single paleoclimate record contains insufficient information to discriminate between typical competing explanatory models. Here we show that, by using the algorithm SMC2 (‘sequential Monte Carlo squared’) combined with novel Brownian‐bridge‐type proposals for the state trajectories, it is possible to estimate Bayes factors to sufficient accuracy...
In observational epidemiological studies, the exposure that is received by an individual often cannot be precisely observed, resulting in measurement error, and a common approach to dealing with measurement error is regression calibration (RC). Use of RC, which requires assumptions about the distribution of unknown error‐free (true) variables, leads to concern about the possibility of bias due to...
Retailers use the vector auto‐regressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available not just for one store, but for a whole chain of stores. We propose to study cross‐category effects by using a multiclass VAR model: we jointly estimate...
Central banks have long used dynamic stochastic general equilibrium models, which are typically estimated by using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off‐the‐shelf dynamic stochastic general equilibrium model applied to quarterly euro...
Environmental epidemiological studies of the health effects of air pollution frequently utilize the generalized additive model (GAM) as the standard statistical methodology, considering the ambient air pollutants as explanatory covariates. Although exposure to air pollutants is multi‐dimensional, the majority of these studies consider only a single pollutant as a covariate in the GAM model. This...
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