Wanting To Be Remembered: Intrinsically Rewarding Work and Generativity in Early Midlife

Previous research on generativity, the desire to leave a legacy through establishing and guiding the next generation, has focused primarily on family life and civic engagement as pathways to midlife generativity. This paper proposes that intrinsically rewarding work can also be associated with a heightened sense of generativity in midlife. We test this hypothesis with data (n = 369 employed individuals, approximately 43 years old) from the 2010 wave of the Edmonton Transitions Study. Civic engagement was positively associated with midlife generativity, as predicted, but the hypothesized positive relationship between generativity and perceived parenting success was not found. Taking into account civic engagement and perceived parenting success, and controlling on a range of other variables, intrinsically rewarding work was positively associated with midlife feelings of generativity.     

Social cognition,social neuroscience,and evolutionary social psychology: What's missing?

In this paper I argue that something important, and something social, is missing from contemporary accounts of social cognition, social neuroscience and evolutionary social psychology. Contemporary accounts of social cognition focus on cognition directed towards social objects, that is, towards persons and social groups. In contrast, early twentieth century accounts of socially engaged cognition focused upon beliefs and attitudes oriented to the represented beliefs and attitudes of members of social ‘reference groups’ and directed towards both social and non‐social objects. I argue that this earlier conception of socially engaged cognition should be integrated with contemporary research on social cognition, social neuroscience and evolutionary social psychology, since it poses a challenge but also an opportunity for these disciplines.     

Key Determinants to using Telehealth Technology to Serve Medically Ill and Depressed Homebound Older Adults

Despite the increasing evidence for the effectiveness of telehealth technology in screening and treating chronic diseases, and comorbid depression among older adults, they have been slowly adopted by home health care (HHC) agencies. Therefore, this study aimed to identify factors that determine telehealth technology adoption. Twenty directors from the National Association for Homecare & Hospice member agencies completed a 45-min telephone interview. Questions were asked regarding their perceptions of telehealth, the key determinants of telehealth adoption and use, and recommendations they would give on telehealth adoption. The majority of the participants perceived telehealth as effective for managing symptoms and reducing cost. Meanwhile, some participants had a mixed feeling toward telehealth for depression care as they did not recognize their agency as equipped with the necessary resources and trained staff. Moreover, significant determinants of telehealth adoption included the agency-related characteristics, the patient-home environment, reimbursement and cost-related factors, and staff telehealth perception. Findings imply that there is a need for financial support both at the state and the federal levels to encourage telehealth adoption among HHC agencies. Future studies should consider exploring strategies used by successful programs to overcome barriers.     

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.     

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....