On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada     

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.     

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.     

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

In survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.     

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.     

Geometries of ‘global’ evangelicalism

In this article, I explore the power dynamics at play in religious place‐making. I critically discuss the uneven co‐configurations of imaginaries of the ‘local’ and ‘global’ within global evangelicalism. Specifically, I analyse the recent recording of a live album by the famous charismatic Australian band Hillsong United (of Hillsong Church) at various locations in Israel‐Palestine, which was followed by a concert tour in Israel. Palestinian evangelical Christians were critical of this endeavour, for they felt that it marginalized and excluded them from their global evangelical faith family. The frictions between the Palestinian evangelical community and Hillsong United illustrate how dominant evangelical actors create an imagination of the ‘local’, which enters the imaginary of global evangelicalism (and bears material consequences). In the article, I thus argue that privileged financial and cultural resources and travel regimes lead to specific notions of geometries of power in global evangelicalism.     

Modeling stereotypes and negative self‐stereotypes as a function of interactions among groups with power asymmetries

Stereotypes is one of the most researched topics in social psychology. Within this context, negative self‐stereotypes pose a particular challenge for theories. In the current work, we propose a model that suggests that negative self‐stereotypes can theoretically be accounted for by the need to communicate in a social system made up by groups with unequal power. Because our theory is dynamic, probabilistic, and interactionist, we use a computational simulation technique to show that the proposed model is able to reproduce the phenomenon of interest, to provide novel accounts of related phenomena, and to suggest novel empirical predictions. We describe our computational model, our variables' dynamic behavior and interactions, and link our analyses to the literature on stereotypes and self‐stereotypes, the stability of stereotypes (in particular, gender and racial stereotypes), the effects of power asymmetries, and the effects of intergroup contact.     

From cognitive dissonance to cognitive Polyphasia: A sociocultural approach to understanding meat‐paradox

Cognitive Dissonance Theory seeks to understand inconsistency by situating it within individual cognition. By doing so, it overlooks the role of the social context in the experience and management of inconsistency and dissonance and fails to capture the processes through which it is negotiated when it appears. On the other side, the cognitive polyphasia hypothesis together with a dialogical approach on Social Representations provide a socioculturally situated, process‐oriented understanding of inconsistency. In this paper, meat‐paradox, the phenomenon of simultaneously declaring love and respect towards animals and also consuming animals, mainly studied through Cognitive Dissonance Theory, is used in order to highlight the merits of a sociocultural approach to inconsistency. Four relevant empirical examples from interviews and focus groups with meat‐eaters and vegetarians in Cyprus are used to illustrate the approach. The examples illustrate how meat‐eaters manage dissonance in ways that exhibit coexistence of contradictory representations and ways of thinking. Three different modalities of knowledge coexistence are identified, as proposed by cognitive polyphasia researchers: displacement, selective prevalence and hybridisation. We discuss the importance of a sociocultural approach to studying paradoxes, the epistemological and methodological implications of such a theorisation and we suggest other life contexts in which such an approach can be applied.     

Social health protection in Cambodia: Challenges of policy design and implementation

The Government of Cambodia is implementing ambitious reform initiatives to improve the country's social health protection system. In January 2018, it was announced that the Health Equity Fund (HEF), which is fully subsidized by a joint government‐donor initiative for the reimbursement of user fees for the poor at public health facilities, is to be expanded to some segments of informal workers belonging to associations, as well as to commune and village chiefs. Since 2017, the National Social Security Fund (NSSF) has provided social health insurance for formal economy workers in enterprises with eight employees or more. In January 2018, it was expanded to civil servants and all employees regardless of the size of the enterprise. However, this article highlights that the new ambitious reforms are not accompanied by careful planning as regards funding, service delivery, human resources and institutional design. This article therefore aims to examine key policy issues and challenges for Cambodia's ambitious reform of its social health protection system in terms of resource generation, population coverage, strategic purchasing and governance.