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Summary. Proteins are the workhorses of all living systems, and protein bioinformatics deals with analysis of protein sequences (one dimensional) and structures (three dimensional). The paper reviews statistical advances in three major active areas of protein structural bioinformatics: structure comparison, Ramachandran plots and structure prediction. These topics play a key role in understanding...
Summary. We consider the problem of transforming values from a flow time series observed over varying time intervals into values that cover calendar intervals such as day, week, month, quarter and year. We call this process calendarization. We propose simple methods based on interpolating the cumulated flows with natural spline interpolations. Alternatively, we provide state space models with missing...
Summary. Cardiovascular ischaemic diseases are one of the main causes of death all over the world. In this class of pathologies, a quick diagnosis is essential for a good prognosis in reperfusive treatment. In particular, an automatic classification procedure based on statistical analysis of teletransmitted electrocardiograph (‘ECG’) traces would be very helpful for an early diagnosis. This work...
Ecological momentary assessment is a method for collecting realtime data in subjects' environments. It often uses electronic devices to obtain information on psychological state through administration of questionnaires at times that are selected from a probability‐based sampling design. This information can be used to model the effect of momentary variation in psychological state on the lifetimes...
Summary. Hierarchical models (HMs) have been used extensively in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for other pollutants and other time varying factors. Recently, the US Environmental Protection Agency has called for research quantifying health effects of simultaneous exposure to many air pollutants. However, straightforward...
Summary. The paper presents a Bayesian approach to analysing interval‐censored data with random unknown end points. Such data occur when the event of interest is interval censored but, because of the measurement process, the interval end points are not known exactly. Modelling the measurement process permits inference that accounts for this source of variability. Our results are motivated by an experimental...
Acute infectious diseases are transmitted over networks of social contacts. Epidemic models are used to predict the spread of emergent pathogens and to compare intervention strategies. Many of these models assume equal probability of contact within mixing groups (homes, schools, etc.), but little work has inferred the actual contact network, which may influence epidemic estimates. We develop a penalized...
Conditional auto‐regressive models are commonly used to capture spatial cor relation in areal unit data, as part of a hierarchical Bayesian model. The spatial correlation structure that is induced by these models is determined by geographical adjacency, but this is too simplistic for some real data sets, which can visually exhibit subregions of strong correlation as well as locations at which the...
Summary. Auditory localization experiments are conducted to evaluate human ability to locate the position of a source of sound, and to determine how population characteristics might affect this ability. These experiments generate data that are circular, bimodal and repeated, and have hypothesized symmetry patterns that should be included and tested within the modelling framework. We propose a two‐part...
Summary. Leading economic indicators are often used to anticipate changes in key economic variables. Understanding the dynamics of these indicators is of primary interest for policy‐making objectives and for sustainable economic welfare. We are concerned with the problem of setting a dynamic threshold above which the value of leading indicators would be considered as extreme. We propose a dynamic...
Summary. Meta‐analysis is often undertaken in two stages, with each study analysed separately in stage 1 and estimates combined across studies in stage 2. The study‐specific estimates are assumed to arise from normal distributions with known variances equal to their corresponding estimates. In contrast, a one‐stage analysis estimates all parameters simultaneously. A Bayesian one‐stage approach offers...
Secondary ion mass spectrometry is a popular physical technique that allows learning elemental and chemical compositions of various substances. In some cases, the mass spectrum of the sample studied is not observable without that of the liquid solution in which the investigated substance must be placed. To separate the informative part of the observed mixed spectrum from the irrelevant liquid solution...
To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with...
Motivated by the need to understand the dynamics of relationship formation and dissolution over time in real world social networks we develop a new longitudinal model for transitions in the relationship status of pairs of individuals (‘dyads’). We first specify a model for the relationship status of a single dyad and then extend it to account for important interdyad dependences (e.g. transitivity—‘a...
The paper develops a framework for volatility graphics in financial time series analysis which allows exploration of the time progression of volatility and the dependence of volatility on past behaviour. It is particularly suitable for identifying volatility structure to be incorporated in specific volatility models. Plotting techniques are identified on the basis of a general time series volatility...
We show how to evaluate a kinship identification system, which is a probabilistic tool devoted to obtain the likelihood ratio supporting the hypothesis that an individual, the candidate for identification, is a specific member of a family, conditional on the available familial DNA evidence. The paper considers the likelihood ratio as a random variable and focuses on the evaluation of the probability...
In the estimation of a lifetime distribution from regular interval‐censored data with an additional censoring variable, we focus on the case where (contrary to the actuarial method) both events (interest and censorship) can occur on a given individual in the same interval and, thus, are observed simultaneously. Specifically, we consider a population where individuals pass through a finite number of...
There is growing interest in understanding the heterogeneity of treatment effects (HTE), which has important implications in treatment evaluation and selection. The standard approach to assessing HTE (i.e. subgroup analyses based on known effect modifiers) is informative about the heterogeneity between subpopulations but not within. It is arguably more informative to assess HTE in terms of individual...
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