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.     

Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.     

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.     

A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring

We propose an approach to determine the distribution of particular linear combinations of hybrid censored order statistics which is based on the calculation of volumes of polytopes. For this purpose, we establish efficient and compact volume formulas in terms of B-splines. Further, we illustrate our approach for ten different progressive hybrid censoring schemes under an exponential assumption.     

Rural Appalachian High School Students’ College‐Going and STEMM Perceptions

The authors examined perceptions of key social cognitive career theory (Lent, Brown, & Hackett, 1994) variables related to college‐going and science, technology, engineering, math, and medical (STEMM) careers in 10th and 11th graders (N = 892) attending 3 rural Appalachian high schools. The authors examined differences in perceptions related to gender, prospective 1st‐generation college student status, and the presence or absence of aspirations to pursue a STEMM career. Young women and young men scored similarly on all but 1 dependent variable, college‐going self‐efficacy (young women scored higher). Students who had STEMM career aspirations had higher scores on every measure than those who did not. Results suggest examining a 3rd prospective 1st‐generation college student status group—students who are unsure of their parents’ education level—as a distinct group in future research. By examining the college‐going and STEMM attitudes of rural Appalachian high school students, this study advances the literature and informs practitioners on reducing educational and vocational inequalities in this region.     

The Career Decision‐Making Difficulties Questionnaire: A Case for Item‐Level Interpretation

The Career Decision‐Making Difficulties Questionnaire (CDDQ; Gati, Krausz, & Osipow, 1996) has long been demonstrated as a useful instrument for career counseling practice. Several case studies have demonstrated how and why using the CDDQ facilitates the career counseling process (e.g., Amir, Gati, & Kleiman, 2008; Gati & Levin, 2014; Levin & Gati, 2014). This study explores how an in‐depth analysis of a case study conducted at the item level can provide career counselors with a richer understanding of their clients’ needs. The case study also emphasizes how administering and providing feedback on the CDDQ in career counseling sessions is likely to foster working alliances. The study concludes by presenting an intervention map based on the CDDQ taxonomy.