A comparison of nonparametric priors in hierarchical mixture modelling for AFT regression

We will pursue a Bayesian nonparametric approach in the hierarchical mixture modelling of lifetime data in two situations: density estimation, when the distribution is a mixture of parametric densities with a nonparametric mixing measure, and accelerated failure time (AFT) regression modelling, when the same type of mixture is used for the distribution of the error term. The Dirichlet process is a popular choice for the mixing measure, yielding a Dirichlet process mixture model for the error; as an alternative, we also allow the mixing measure to be equal to a normalized inverse-Gaussian prior, built from normalized inverse-Gaussian finite dimensional distributions, as recently proposed in the literature. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. A comparison between the two models will be carried out on the grounds of their predictive power and their ability to identify the number of components in a given mixture density.     

Forecasting obesity trends in England

Summary.  Forecasts of trends in obesity in England for 2010 are produced by treating the available data, which contain the proportions of the population, categorized by age and sex, falling into different body mass index ranges, as compositional data sets, so that the implicit simplex restrictions are automatically satisfied. Forecasts are calculated by using linear trend models for the log-ratio transformations and are accompanied by prediction regions. The advantages of treating data on proportions compositionally are emphasized and compared with forecasts that have been obtained by ignoring this restriction.     

Stagnation only on the surface? The implications of skill and family responsibilities for the gender wage gap in Sweden, 1974–2010

The wage differential between women and men persists in advanced economies despite the inflow of women into qualified occupations in recent years. Using five waves of the Swedish Level‐of‐Living Survey (LNU), this paper explores the gender wage gap in Sweden during the 1974–2010 period overall and by skill level. The empirical analyses showed that the general gender wage gap has been nearly unchanged for the past 30 years. However, the gender difference in wage in less qualified occupations fell considerably, whereas the gender pay gap remained stable for men and women in qualified occupations. The larger significance of family responsibilities for wages in qualified occupations is one likely explanation for this result.     

Occupational Sex Composition and Gendered Housework Performance: Compensation or Conventionality?

This article examines the association between occupational sex composition and housework, considering total housework time, time on male‐typed and female‐typed tasks, and the percent of total time spent on male and female tasks. Previous research examining male‐ and female‐typed chores independent of total housework suggests that couples compensate for gender‐atypical employment through gender‐typical housework performance, but this analysis of the National Survey of Families and Households (1992–1994) and the American Time Use Survey (2003–2013) demonstrates that assuming a quadratic association and failing to contextualize gendered housework performance within total housework performance obscures the true relationship between occupation and housework. In fact, women and men in gender‐atypical occupations perform a more gender‐atypical combination of chores. The influence of gender deviance neutralization in the housework literature may overshadow alternative explanations and model specifications. In particular, by assuming a quadratic association, researchers may impose, rather than test, gender deviance neutralization.     

Youth studies,citizenship and transitions: towards a new research agenda

A key goal in youth studies is to gain holistic understandings of what it means to be young. However, a significant impediment to achieving this has been the tendency of youth studies to develop along siloed and stratified subfields. In keeping with the goal of creating more productive dialogue between subfields in youth studies, this paper examines the intersections between research in youth citizenship and youth transitions to consider the fresh insights and cross fertilisations that such an analysis may yield. This examination reveals a sense of dissatisfaction in both subfields with traditional normative and linear models of citizenship and transitions which rely on step-wise and sequential notions of time. In response, the paper advances a new research agenda which posits more temporally, spatially and relationally-sensitive understandings of youth citizenship and transition. Drawing on Ingold (2007. Lines: A brief history. London: Routledge), this agenda proposes the use of three alternative metaphors – genealogical, wayfaring and threads – which could hold the potential to unsettle the normativity and linearity of previous youth transitions and citizenship frameworks, and thus provide deeper insights into what it means to live and to be young citizens in times of transition.     

Flexible objective Bayesian linear regression with applications in survival analysis

We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general non-informative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in [16]. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.     

Unified Inference for Sparse and Dense Longitudinal Data in Time‐varying Coefficient Models

Time‐varying coefficient models are widely used in longitudinal data analysis. These models allow the effects of predictors on response to vary over time. In this article, we consider a mixed‐effects time‐varying coefficient model to account for the within subject correlation for longitudinal data. We show that when kernel smoothing is used to estimate the smooth functions in time‐varying coefficient models for sparse or dense longitudinal data, the asymptotic results of these two situations are essentially different. Therefore, a subjective choice between the sparse and dense cases might lead to erroneous conclusions for statistical inference. In order to solve this problem, we establish a unified self‐normalized central limit theorem, based on which a unified inference is proposed without deciding whether the data are sparse or dense. The effectiveness of the proposed unified inference is demonstrated through a simulation study and an analysis of Baltimore MACS data.     

Estimating function method for product autoregressive models

The first-order product autoregressive (PAR(1)) model introduced by McKenzie in 1982 McKenzie, E. D. (1982). Product autoregression: A time series characterization of the gamma distribution. Journal of Applied Probability 19:463–468. [Google Scholar] did not attract the attention of practitioners due to the unavailability of a proper estimation method. This article proposes an estimating function (EF) method to fill the gap. In particular, we suggest an optimal combination of linear and quadratic EFs to overcome the problem of parameter identification. The procedure is applied to Weibull and Gamma PAR(1) models. Simulation and data analysis show that the proposed method performs better than the existing methods.     

Monitoring the coefficient of variation using a variable sample size and sampling interval control chart

This article proposes a CV chart by using the variable sample size and sampling interval (VSSI) feature to improve the performance of the basic CV chart, for detecting small and moderate shifts in the CV. The proposed VSSI CV chart is designed by allowing the sample size and the sampling interval to vary. The VSSI CV chart's statistical performance is measured by using the average time to signal (ATS) and expected average time to signal (EATS) criteria and is compared with that of existing CV charts. The Markov chain approach is employed in the design of the chart.