Testing for No Effect by Cosine Series Methods

The properties of three lack-of-fit tests that are related to non-parametric cosine regression analysis are examined in the context of testing for a constant mean function. Analytic power comparisons of these tests vs a most powerful test are made using intermediate asymptotic relative efficiency. In particular, a data-driven test is produced which is asymptotically as efficient as the most powerful test over a class of alternatives. A small scale simulation experiment is conducted to ascertain the extent that the large sample comparisons are applicable to finite samples.     

A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

Standard errors for EM estimation

The EM algorithm is a popular method for computing maximum likelihood estimates. One of its drawbacks is that it does not produce standard errors as a by-product. We consider obtaining standard errors by numerical differentiation. Two approaches are considered. The first differentiates the Fisher score vector to yield the Hessian of the log-likelihood. The second differentiates the EM operator and uses an identity that relates its derivative to the Hessian of the log-likelihood. The well-known SEM algorithm uses the second approach. We consider three additional algorithms: one that uses the first approach and two that use the second. We evaluate the complexity and precision of these three and the SEM in algorithm seven examples. The first is a single-parameter example used to give insight. The others are three examples in each of two areas of EM application: Poisson mixture models and the estimation of covariance from incomplete data. The examples show that there are algorithms that are much simpler and more accurate than the SEM algorithm. Hopefully their simplicity will increase the availability of standard error estimates in EM applications. It is shown that, as previously conjectured, a symmetry diagnostic can accurately estimate errors arising from numerical differentiation. Some issues related to the speed of the EM algorithm and algorithms that differentiate the EM operator are identified.     

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.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

Talking Turkey: Effects of Family Discussions About the 2016 Election over the Thanksgiving Holiday

Four hundred and seventy-nine undergraduates reported on family discussions about the 2016 election that occurred over that same year’s Thanksgiving break. Family Communication Patterns Theory was used to predict the perceived outcomes of such discussion on family members. Higher conversation orientation predicted a more positive perceived effect of political talk on family closeness. Perceived similarity of political views partially moderated the relationship between conformity orientation and perceived effect of political talk on family closeness. Reported stress levels since the election were negatively predicted by the perceived (positive) effect of political talk on family closeness. Practical and theoretical implications for families and political socialization processes are discussed.     

Disrupting Sexual Norms: An Application of the Critical Interpersonal and Family Communication (CIFC) Framework in the Context of Vulvodynia

This study uses intersectional methodology to critically explore vulvodynia, chronic female genital pain, as it disrupts norms governing gender roles in heterosexual sex and reproduction. Interviews with 26 women with vulvodynia illuminate core tenets of the critical interpersonal and family communication (CIFC) framework, which include power; private and public sphere bidirectionality; the critique, resistance, and transformation of the status quo; and considerations of author reflexivity. As the author is herself a heterosexual woman navigating vulvodynia, reflexivity is particularly salient. Status quo social constructs are explicated. Results demonstrate how participants experience losses of relational power and problematic media depictions establishing standards for “normal” heterosexual sex. Discourses and practices concerning the perpetuation, resistance, and potential transformation of the status quo are described.     

The white native speaker and inequality regimes in the private English language school

Private English language schools market the language as a tool that helps one connect with others from different cultures. Despite their promotion of English aiding in intercultural communication, these institutions may believe that only the white native speaker is the ideal teacher of the language. This valuing of the white native speaker can consequently act as an organisational inequality regime that marginalises nonwhite teachers. Using qualitative interviews with 10 nonwhite instructors working in schools in Toronto, Canada, this article investigates the ways in which these teachers experience the inequality regime of the white native speaker at work. The findings indicate that the teachers experience this inequality regime as a series of microaggressions that involve space, competence and customer desire. The article concludes with suggestions to dismantle inequality regimes in private institutions.     

Self-Stigma of seeking help and job burnout in mental health care providers: the comparative study of Lithuanian and the USA samples

This study aimed to explore the correlation between job burnout and self-stigma of seeking help among nonmedical mental health care providers (psychologists, social workers, and counselors) in two countries – Lithuania and the US. The study included 234 professionals (111 social workers and 123 psychologists) from Lithuania and 93 professionals (33 counselors, 23 social workers, and 37 psychologists) from the US on a voluntary basis (93% females, mean age – 39.81?years). They completed a self-reported questionnaire with the Self-Stigma of Seeking Help Scale (SSOSH) and the Maslach Burnout Inventory-General Survey (MBI-GS). The results revealed a statistically significant positive correlation between self-stigmatization and burnout in the Lithuanian sample, but only weak positive correlation between depersonalization and self-stigma of seeking help in the US sample. The relationship between the self-stigma of seeking help and burnout was stronger in the Lithuanian sample of professionals when compared to their colleagues in the US.