A New Method for Determining Why Length of Life is More Unequal in Some Populations Than in Others

Why is there greater variability in individual longevity in some populations than in others? We propose a decomposition method designed to address that question by quantifying the effects of population differences in the spread, allocation, and timing of the principal causes of death. Applying the method to the United States and Sweden, we find that spread effects account for about two-thirds of the greater variance in age at death among American adults, meaning that two-thirds of the U.S.-Sweden difference would persist if the two countries differed only with respect to within-cause variance among adults. The remainder of the difference is due largely to allocation effects, with the greater incidence of homicides and fatal traffic accidents alone accounting for more than one-fourth of the greater variance in age at death among adults in the United States.     

Effectiveness of potent antiretroviral therapy on progression of human immunodeficiency virus: Bayesian modelling and model checking via counterfactual replicates

Summary.  We analyse data from a seroincident cohort of 457 homosexual men who were infected with the human immunodeficiency virus, followed within the multicentre Italian Seroconversion Study. These data include onset times to acquired immune deficiency syndrome (AIDS), longitudinal measurements of CD4⁺ T-cell counts taken on each subject during the AIDS-free period of observation and the period of administration of a highly active antiretro- viral therapy (HAART), for the subset of individuals who received it. The aim of the study is to assess the effect of HAART on the course of the disease. We analyse the data by a Bayesian model in which the sequence of longitudinal CD4⁺ cell count observations and the associated time to AIDS are jointly modelled at an individual subject's level as depending on the treatment. We discuss the inferences obtained about the efficacy of HAART, as well as modelling and computation difficulties that were encountered in the analysis. These latter motivate a model criticism stage of the analysis, in which the model specification of CD4⁺ cell count progression and of the effect of treatment are checked. Our approach to model criticism is based on the notion of a counterfactual replicate data set Z ^c . This is a data set with the same shape and size as the observed data, which we might have observed by rerunning the study in exactly the same conditions as the actual study if the treated patients had not been treated at all. We draw samples of Z ^c from a null model M ₀, which assumes absence of treatment effect, conditioning on data collected in each subject before initiation of treatment. Model checking is performed by comparing the observed data with a set of samples of Z ^c drawn from M ₀.     

Subexponential Distributions — Large Deviations with Applications to Insurance and Queueing Models

This paper presents a fine large‐deviations theory for heavy‐tailed distributions whose tails are heavier than exp(?√t and have finite second moment. Asymptotics for first passage times are derived. The results are applied to estimate the finite time ruin probabilities in insurance as well as the busy period in a GI/G/1 queueing model.     

Bayesian model selection for D‐vine pair‐copula constructions

In recent years analyses of dependence structures using copulas have become more popular than the standard correlation analysis. Starting from Aas et al. ( 2009 ) regular vine pair‐copula constructions (PCCs) are considered the most flexible class of multivariate copulas. PCCs are involved objects but (conditional) independence present in data can simplify and reduce them significantly. In this paper the authors detect (conditional) independence in a particular vine PCC model based on bivariate t copulas by deriving and implementing a reversible jump Markov chain Monte Carlo algorithm. However, the methodology is general and can be extended to any regular vine PCC and to all known bivariate copula families. The proposed approach considers model selection and estimation problems for PCCs simultaneously. The effectiveness of the developed algorithm is shown in simulations and its usefulness is illustrated in two real data applications. The Canadian Journal of Statistics 39: 239–258; 2011 © 2011 Statistical Society of Canada     

Max-stable processes for modeling extremes observed in space and time

Max-stable processes have proved to be useful for the statistical modeling of spatial extremes. For statistical inference it is often assumed that there is no temporal dependence; i.e., that the observations at spatial locations are independent in time. In a first approach we construct max-stable space–time processes as limits of rescaled pointwise maxima of independent Gaussian processes, where the space–time covariance functions satisfy weak regularity conditions. This leads to so-called Brown–Resnick processes. In a second approach, we extend Smith’s storm profile model to a space–time setting. We provide explicit expressions for the bivariate distribution functions, which are equal under appropriate choice of the parameters. We also show how the space–time covariance function of the underlying Gaussian process can be interpreted in terms of the tail dependence function in the limiting max-stable space–time process.     

A note On outlier sensitivity of Sliced Inverse Regression

Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Several properties of this method have been examined already, but little attention has been paid to robustness aspects. In this article, we focus on the sensitivity of SIR to outliers and show in what sense and how severely SIR can be influenced by outliers in the data.     

Joint projections of temperature and precipitation change from multiple climate models: a hierarchical Bayesian approach

Summary.  Posterior distributions for the joint projections of future temperature and precipitation trends and changes are derived by applying a Bayesian hierachical model to a rich data set of simulated climate from general circulation models. The simulations that are analysed here constitute the future projections on which the Intergovernmental Panel on Climate Change based its recent summary report on the future of our planet's climate, albeit without any sophisticated statistical handling of the data. Here we quantify the uncertainty that is represented by the variable results of the various models and their limited ability to represent the observed climate both at global and at regional scales. We do so in a Bayesian framework, by estimating posterior distributions of the climate change signals in terms of trends or differences between future and current periods, and we fully characterize the uncertain nature of a suite of other parameters, like biases, correlation terms and model-specific precisions. Besides presenting our results in terms of posterior distributions of the climate signals, we offer as an alternative representation of the uncertainties in climate change projections the use of the posterior predictive distribution of a new model's projections. The results from our analysis can find straightforward applications in impact studies, which necessitate not only best guesses but also a full representation of the uncertainty in climate change projections. For water resource and crop models, for example, it is vital to use joint projections of temperature and precipitation to represent the characteristics of future climate best, and our statistical analysis delivers just that.     

A Turning Point in Gender Bias in Mortality? An Update on the Number of Missing Women

Amartya Sen started a debate about gender bias in mortality by estimating the number of “missing women,” which refers to the number of females of any age who have presumably died as a result of discriminatory treatment. Depending on the assumptions made, the combined estimates for countries exhibiting the presence of such gender bias varied between 60 and 107 million. As new population data have become available for these countries, this article examines whether the number of “missing women” has changed in the past decade. The combined estimate of the number of missing women has risen in absolute terms but has fallen slightly in relation to overall population. Considerable improvement is evident in West Asia, North Africa, and parts of South Asia, while only small improvements have occurred in India and a deterioration took place in China. Analyses of the underlying causes of gender bias in mortality suggest that improvements are largely related to improved female education and employment opportunities and rising overall incomes, while deterioration is mostly attributable to the rising incidence of sex‐selective abortions.     

Mapping the Nomological Network of Career Self-Efficacy

This article examined the degree to which personality hardiness (control, commitment, and challenge), career beliefs (status, preference, motivation, and flexibility), self-efficacy (general and social), and occupational choice status related to career self-efficacy among a sample of 181 college juniors and seniors. Results from an intercorrelation matrix and regression analysis indicated that motivation, self-efficacy, occupational choice status, and commitment correlated significantly to career self-efficacy. Applications of these findings to career counseling are discussed.