Multivariate Kendall's tau for change‐point detection in copulas

Statistical procedures for the detection of a change in the dependence structure of a series of multivariate observations are studied in this work. The test statistics that are proposed are $L_1$ , $L_2$ , and $L_{\infty }$ distances computed from vectors of differences of Kendall's tau; two multivariate extensions of Kendall's measure of association are used. Since the distributions of these statistics under the null hypothesis of no change depend on the unknown underlying copula of the vectors, a procedure based on the multiplier central limit theorem is used for the computation of p‐values; the method is shown to be valid both asymptotically and for moderate sample sizes. Alternative versions of the tests that take into account possible breakpoints in the marginal distributions are also investigated. Monte Carlo simulations show that the tests are powerful under many scenarios of change‐point. In addition, two estimators of the time of change are proposed and their efficiency is carefully studied. The methodologies are illustrated on simulated series from the Canadian Regional Climate Model. The Canadian Journal of Statistics 41: 65–82; 2013 © 2012 Statistical Society of Canada     

What motivates adolescent bystanders to intervene when immigrant youth are bullied?

Pathways to bystander responses were examined in both generalized and bias-based bullying incidents involving immigrant-origin victims. Participants were 168 (M_age = 14.54, 57% female) adolescents of immigrant (37.5%) and nonimmigrant backgrounds, who responded to their likelihood of intervening on behalf of either an Arab or Latine victim. Models tested whether contact with immigrants and one's desires for social contact with immigrant-origin peers mediated the effects of individual (shared immigrant background, and discriminatory tendencies) and situational (inclusive peer norms) intergroup factors on active bystander responses. Findings indicated that desires for social contact reliably mediated effects across both victims; however, contact with immigrant peers was only associated with responses to Latine victims. Implications for how to promote bystander intervention are discussed.     

On the almost unbiased generalized liu estimator and unbiased estimation of the bias and mse

In this paper, we derive the almost unbiased generalized Liu estimator and examine an exact unbiased estimator of the bias and mean squared error of the feasible generalized Liu estimator . We compare the almost unbiased generalized Liu estimator (AUGLE) with the generalized Liu estimator (GLE) and with the ordinary least squares estimator (OLSE).     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.     

Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields

We investigate the asymptotic behaviour of binned kernel density estimators for dependent and locally non-stationary random fields converging to stationary random fields. We focus on the study of the bias and the asymptotic normality of the estimators. A simulation experiment conducted shows that both the kernel density estimator and the binned kernel density estimator have the same behavior and both estimate accurately the true density when the number of fields increases. We apply our results to the 2002 incidence rates of tuberculosis in the departments of France.     

Multivariate forests with missing mixed outcomes

In this article, we propose a multivariate random forest method for multiple responses of mixed types with missing responses. Imputation is performed for each bootstrap sample used to build the individual trees that form the forest. The individual trees are built using a weighted splitting rule allowing downweighting of imputed observations. A simulation study shows the benefits of this approach over complete case analysis when missing responses are missing completely at random and missing at random (MAR). In particular, the gain in prediction accuracy of the proposed method is larger in the MAR case and also increases as the proportion of missing increases.     

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis

We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal component method dedicated to categorical data: multiple correspondence analysis (MCA). The uncertainty concerning the parameters of the imputation model is reflected using a non-parametric bootstrap. Multiple imputation using MCA (MIMCA) requires estimating a small number of parameters due to the dimensionality reduction property of MCA. It allows the user to impute a large range of data sets. In particular, a high number of categories per variable, a high number of variables or a small number of individuals are not an issue for MIMCA. Through a simulation study based on real data sets, the method is assessed and compared to the reference methods (multiple imputation using the loglinear model, multiple imputation by logistic regressions) as well to the latest works on the topic (multiple imputation by random forests or by the Dirichlet process mixture of products of multinomial distributions model). The proposed method provides a good point estimate of the parameters of the analysis model considered, such as the coefficients of a main effects logistic regression model, and a reliable estimate of the variability of the estimators. In addition, MIMCA has the great advantage that it is substantially less time consuming on data sets of high dimensions than the other multiple imputation methods.     

A transnational practice between fractured homes: Second-generation Turkish–German migrants’ experiences of visiting and being visited

The paper explores the multiple ways in which visits affect the understanding of home for the Turkish–German second generation who have relocated to Turkey. Based on thematic–narrative analysis of 116 life-story interviews with second-generation ‘returnees’ in five regions of Turkey, three types of visits are identified: (i) family visits to Turkey whilst growing up in Germany; (ii) visits to Germany after the second generation has ‘returned’ to Turkey; (iii) visits to Turkey by the second generation's Germany-residing relatives and friends. Each type has different meanings for the visitors and the visited, creating fluid reflections on the meaning of home, which, especially for the second-generation ‘returnees’, tends to become fractured. Constantly comparing their two home(-land)s since childhood, they often simultaneously feel both ‘here’ and ‘there’ as a result of changing attachments and a mix of positive and negative experiences in both locales with their families, friends and the dominant others.     

“Having a family is the new normal”: Parenting in neoliberal academia during the COVID-19 pandemic

The outbreak of the COVID-19 pandemic has made explicit the burden of care shouldered by academic mothers, in addition to juggling their scholarly commitments. Although discussions are abundant on the impact of caring responsibilities on the careers of women academics, neoliberal academia continues to minimize such struggles. Despite the disruptions to family routines caused by the health crisis, academic institutions have expected academic mothers and fathers to continue undertaking their professional responsibilities at the same level as before, disregarding their parenting demands. This paper contributes to the research on parenthood in academia by looking at how, throughout the pandemic, academic parents have negotiated the tensions between parenthood and academic demands, and by investigating the strategies they use to confront neoliberal culture of academic performativity, even amid the health crisis. The paper engages with the “space invaders” concept used by Puwar (2004) to analyze the “hypervisibility” of academic mothers' and fathers' “bodies out of place” during the pandemic, and to investigate their “renegade acts” against the uncaring attitudes of their institutions. Evidence is drawn from a qualitative study conducted during December 2020 and January 2021 among scholars affiliated to Portuguese academic institutions: 17 in-depth interviews conducted with women, and two mixed-gender focus groups. Our results research reveal how the experiences of academic mothers and fathers were not uniform during the pandemic. In addition, it shows how, despite their commitment to their academic responsibilities, these parents have crafted various resistance strategies to confront the institutional pressure to continue maintain their working routines, and instead positioning themselves as “more than just academics.”