Dual experiences of teenage parenting and homelessness among Native Hawaiian youth: A critical interpretative phenomenological analysis

Teenage parenting is a challenging venture—particularly when parenting while homeless. The present study explores in‐depth through case‐based analysis the lived experiences of three young homeless and parenting teens (one mother and two fathers). It is the first known study of teenage parenting and homelessness among Native Hawaiian youth, a key affected population. Interpretative phenomenological analysis of participants' individual interviews revealed three themes that characterize the experience of parenting as a teen while homeless: (a) They never raised me up (childhood experiences); (b) becoming mommy and daddy (early experiences of teen pregnancy and parenting while homeless); and (c) finding our place (current experiences of teen parenting and perspectives on teen pregnancy and homelessness). Implications for direct practice, social policy, and future research are discussed in the context of this complex reality.     

The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops

This study investigates the impact of worker learning, worker flexibility, and labor attrition on the system performance of a dual resource constrained (DRC) job-shop. The effects of learning and labor attrition have not been previously addressed in DRC literature. Results from the study, consistent with previous literature, show that the greatest benefits are achieved when inter-departmental worker flexibility is incrementally introduced into the system. In addition, the learning environment, which depends on the initial processing time of jobs and the learning rates of workers, is shown to impact the acquisition of flexibility. The study also shows that the impact of labor attrition on system performance under certain shop conditions may be significant.     

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.     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

A new aging concept derived from the increasing convex ordering

In this paper, a comparison between the life distribution of a new unit with that of a used unit in the increasing convex order is made leading to a new class of life distributions which we call “new better than used in convex ordering of second order”. This class includes as subclasses the NBU and the NBUC and is a subclass of the NBUCA class. Preservation properties under convolution, random maxima, mixing and formation of coherent structures are established. Stochastic comparisons of the excess lifetime when the inter-arrival times belong to the NBUC(2) class are developed. Some applications of Poisson shock models and a test of exponentiality against NBUC(2) alternative are presented.     

A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

Protecting and Benchmarking Migrants’ Rights: An Analysis of the Global Compact for Safe,Orderly and Regular Migration

The Global Compact for Safe, Orderly, and Regular Migration (GCM) was to be “guided by human rights law and standards” in recognition of the rights of international migrants, who are currently protected by an overlapping patchwork of treaties and international law. The GCM contains many laudable commitments that, if implemented, will ensure that states more consistently respect, protect, and fulfil the rights of all migrants and also that states incorporate data on migration into a more cohesive governance regime that does more to promote cooperation on the issue of international migration. However, many concerns remain. Using a legal analysis and cross‐national policy data, we find that the GCM neither fully articulates existing law nor makes use of international consensus to expand the rights of migrants. In its first section, this article provides a concise analysis of the GCM's compliance with a set of core principles of existing international human rights law regarding migrants. In the second section, we apply a novel instrument to create an objective, cross‐national accounting of the laws protecting migrants’ rights in various national legal frameworks. Focusing on a sample of five diverse destination and sending countries, the results suggest we are close to an international consensus on the protection of a core set of migrants’ rights. This analysis should help prioritize the work necessary to implement the GCM.