Pruning a sufficient dimension reduction with a p-value guided hard-thresholding

Principal fitted component (PFC) models are a class of likelihood-based inverse regression methods that yield a so-called sufficient reduction of the random p-vector of predictors X given the response Y. Assuming that a large number of the predictors has no information about Y, we aimed to obtain an estimate of the sufficient reduction that ‘purges’ these irrelevant predictors, and thus, select the most useful ones. We devised a procedure using observed significance values from the univariate fittings to yield a sparse PFC, a purged estimate of the sufficient reduction. The performance of the method is compared to that of penalized forward linear regression models for variable selection in high-dimensional settings.     

Marital Sorting and Parental Wealth

The extent of marital sorting by socioeconomic background has implications for the intergenerational transmission of inequality, the role of marriage as a mechanism for social mobility, and the extent of cross-group interactions within a society. However, studies of assortative mating have disproportionately focused on spouses’ education, rather than their social origins. Using data from the Panel Study of Income Dynamics (PSID), and exploiting the unique genealogical design of the data set, we study the degree to which spouses sort on the basis of parental wealth. We find that the estimated correlation in parental wealth among married spouses, after controlling for race and age, is about .4. Importantly, we show that controlling for spousal education explains only one-quarter of sorting based on parental wealth. We show that our results are robust to accounting for measurement error in spousal reports of parental wealth and for selection into and out of marriage.     

Minimum average deviance estimation for sufficient dimension reduction

Sufficient dimension reduction methods aim to reduce the dimensionality of predictors while preserving regression information relevant to the response. In this article, we develop Minimum Average Deviance Estimation (MADE) methodology for sufficient dimension reduction. The purpose of MADE is to generalize Minimum Average Variance Estimation (MAVE) beyond its assumption of additive errors to settings where the outcome follows an exponential family distribution. As in MAVE, a local likelihood approach is used to learn the form of the regression function from the data and the main parameter of interest is a dimension reduction subspace. To estimate this parameter within its natural space, we propose an iterative algorithm where one step utilizes optimization on the Stiefel manifold. MAVE is seen to be a special case of MADE in the case of Gaussian outcomes with a common variance. Several procedures are considered to estimate the reduced dimension and to predict the outcome for an arbitrary covariate value. Initial simulations and data analysis examples yield encouraging results and invite further exploration of the methodology.     

‘They'd beat us with whatever is available to them’: Exploitation and abuse of Ghanaian domestic workers in the Middle East

Generic accounts of pervasive cases of exploitation and abuse against migrant domestic workers in the Middle East exist in the extant literature. However, very little is known about the breadth, depth and gendered nature of abuses experienced by female migrants from especially the sub-Saharan African region. Abuses of the rights of sub-Saharan Africans are under-represented and under-theorised. This paper interrogates the question what is the nature, extent and severity of exploitation and abuse against female Ghanaian domestic workers in the Middle East? Using data from mixed-methods research, this paper adopts the framework of structural, symbolic and interpersonal violence to examine the range of abuses against domestic workers and the context within which these abuses take place.