On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

On approximations via convolution-defined mixture models

An often-cited fact regarding mixing or mixture distributions is that their density functions are able to approximate the density function of any unknown distribution to arbitrary degrees of accuracy, provided that the mixing or mixture distribution is sufficiently complex. This fact is often not made concrete. We investigate and review theorems that provide approximation bounds for mixing distributions. Connections between the approximation bounds of mixing distributions and estimation bounds for the maximum likelihood estimator of finite mixtures of location-scale distributions are reviewed.     

OLS and IV estimation of regression models including endogenous interaction terms

We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.     

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.     

Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion

Lifetime Data Analysis - This work was motivated by observational studies in pregnancy with spontaneous abortion (SAB) as outcome. Clearly some women experience the SAB event but the rest do not....     

The Mediating Effect of Resilience Between Social Support for Exercise and Resident Satisfaction in Assisted Living

This study examined whether residents' level of resilience mediated the relationship between social support for exercise from staff and resident satisfaction in assisted living. This was a secondary data analysis using baseline data from a function-focused care intervention study including 171 residents from four assisted living facilities. Using structural equation modeling, we found that mood and social support for exercise from staff were the only variables associated with resilience. Mood, gender, cognition, and social support for exercise from staff directly influenced resident satisfaction and explained 31% of the variance in the model. Implications for future research and practice are discussed.     

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.     

Disparities in Healthcare Utilization Among Adults with Obesity in the United States,Findings from the NHIS: 2006–2015

Population Research and Policy Review - Few studies of disparities consider logistical hurdles faced by adults with obesity in obtaining needed healthcare. This study compared adults with obesity...