Juggling Between Parental and School Expectations: The Development of Domain‐Specific Acculturation Orientations in Early Adolescence

We examined how perceived acculturation expectations from parents and school, and ethnic discrimination predicted early adolescents’ heritage and mainstream acculturation orientations at home (private domain) and in school (public domain) one year later. We surveyed 263 early adolescents of immigrant background in Germany (M_age = 10.44 years, 60% female). Multigroup path analyses revealed that perceived acculturation expectations and ethnic discrimination were more strongly related to adolescents’ private than public acculturation orientations. Parental heritage expectations were the strongest predictor of adolescents’ acculturation orientations. Boys were more susceptible than girls to ethnic discrimination and acculturation expectations in school, which affected their private and public acculturation orientations. Results highlight the importance of integrating domain‐specific and gendered experiences when analyzing adolescents’ acculturative development.     

Accurate estimation for extra-Poisson variability assuming random effect models

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.     

A new regression model for bimodal data and applications in agriculture

We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.     

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.     

“Migrant Capital” and Domestic Work: Labour Trajectories of Immigrant Women in Spain

This article analyses the labour trajectory of migrant women in domestic service. The research considers women's working conditions upon arrival, or their “migrant capital” (i.e. their human, social and economic capital) as the defining factors in their labour trajectories. The study, conducted on a sample of migrant women in domestic service, reveals the different value each type of capital has at each stage of a labour trajectory. The social network is the core capital in their first job. Nevertheless, the key factors in labour mobility are human capital and a household's financial needs. The processes of administrative regularization and family reunification prompt far‐reaching changes in these women's labour trajectories. Finally, labour trajectories during the economic crisis have been shaped by financial needs, leading to a decapitalization of the human capital acquired, with even legal status surrendering its value.