A qualitative case study of a Korean former addict’s existential self‐interpretation and identity transformation

This study explored the case of a former addict, focusing on his existential self‐interpretation and identity transformation process. This study began from the question of whether philosophical processes related to existential self‐interpretation could lead to the formation of the concept of bottoming out and of identity transformation. To answer this question, the study analyzed the case of a male in his mid‐forties who was addicted to drugs for approximately 30 years and has only recently maintained abstinence. The study used the phenomenological approach to determine the meaning drugs held for him in each stage, and what drug‐related identity he had in each stage. Further, this study identified the integrative implications of these meanings in terms of existential self‐interpretation and identity transformation. The four different identities the man identified for the four stages of his life were juvenile delinquent, gang member, half‐gangster, and breadwinner, and the associated meanings drugs had for him during each period were “glue,” “filthy,” “leeches,” and “abstinence,” respectively. Finally, this study elucidated the integration of these meanings through the hermeneutic circle, identified the social welfare implications of the findings, and developed educational recommendations.     

Megachurch involvement at the local level: Examining conditions for church action in economic development

The roles of small congregations and their social impacts are well known, but few theoretical studies investigate megachurch congregations. These mammoth organizations have the potential for advancing social good, but minimal research specifically focuses on the activities of these congregations in economic development (ED). This study addresses this dearth of information by answering the following research questions: (a) In what ways are megachurches involved in ED activities? (b) Who benefits from these programs and how are the programs offered? and (c) What factors influence megachurches to offer ED activities? We frame our investigation using both demand‐ and supply‐side arguments from Frumkin's (2002) model of nonprofit and voluntary action. Online survey data and follow‐up interviews with 42 megachurch leaders in the Dallas/Fort Worth and greater Houston areas show that megachurches are implementing ED activities in alignment with demand‐side explanations, especially when advancing opportunities for the unemployed and underemployed. However, supply‐side arguments provide more explanatory power, indicating that volunteer engagement with ED activities plays a significant role in the provision of ED services through megachurches. Our study offers a better understanding of why megachurches carry out ED activities with considerable attention devoted to how megachurches partner with other congregations, nonprofits, and government entities to encourage economic growth and improve the economic well‐being of community members.     

Trade union pedagogy and cross‐border action

In this article, we examine the potential of global union pedagogy to address the structural and political challenges of cross‐border trade‐union action. We do so by proposing an analytical framework that draws on labour relations, political sociology and education to explain educational processes and outcomes as responses to the pitfalls of global labour campaigns and the inadequacy of global and local labour institutions. We proceed to assess the value of our framework by elaborating on its different dimensions – framing, synthesizing, connecting and regenerating – in relation to the educational work of a global union federation, namely the International Transport Workers' Federation. We find that an actor‐centred approach that combines top–down, bottom–up as and horizontal processes of collecting knowledge from different contexts and making links between different countries, industries and parts of supply chains can help actors realize that their seemingly diverse concerns are essentially different manifestations of the same problem.     

First‐generation migrants become grandparents: how migration backgrounds affect intergenerational relationships

First‐generation migrants are not only reaching the age of retirement, but they are also becoming grandparents in the host society. Through the specific case of Portuguese migrants in Luxembourg, we look at what point their migration background becomes important to their intergenerational relationships. We have acquired the data for this article from biographical narrative interviews with ten older first‐generation grandparents and ten second‐generation parents. On the basis of these findings, we address three important aspects of intergenerational relations: (i) how first‐generation migrants socialize their grandchildren, (ii) grandparental mobility patterns and their embeddedness in practices of socialization and care, and (iii) the challenges produced by language in intergenerational relationships.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

A general central limit theorem and a subsampling variance estimator for α‐mixing point processes

We establish a central limit theorem for multivariate summary statistics of nonstationary α‐mixing spatial point processes and a subsampling estimator of the covariance matrix of such statistics. The central limit theorem is crucial for establishing asymptotic properties of estimators in statistics for spatial point processes. The covariance matrix subsampling estimator is flexible and model free. It is needed, for example, to construct confidence intervals and ellipsoids based on asymptotic normality of estimators. We also provide a simulation study investigating an application of our results to estimating functions.     

Cox regression of clustered event times with covariates missing not at random

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.