Adaptive Estimation of the Integral of Squared Regression Derivatives

A problem of estimating the integral of a squared regression function and of its squared derivatives has been addressed in a number of papers. For the case of a heteroscedastic model where smoothness of the underlying regression function, the design density, and the variance of errors are known, the asymptotically sharp minimax lower bound and a sharp estimator were found in Pastuchova & Khasminski (1989). However, there are apparently no results on the either rate optimal or sharp optimal adaptive, or data-driven, estimation when neither the degree of regression function smoothness nor design density, scale function and distribution of errors are known. After a brief review of main developments in non-parametric estimation of non-linear functionals, we suggest a simple adaptive estimator for the integral of a squared regression function and its derivatives and prove that it is sharp-optimal whenever the estimated derivative is sufficiently smooth.     

A green space vision in Southeast Michigan’s most heavily industrialized area

Urban Ecosystems - In 2011, Marathon Petroleum Company LP, which has operated a refinery west of the Rouge River since 1959, initiated a voluntary property purchase program in the neighborhood...     

Variation in age,body size,and reproductive traits among urban and rural amphibian populations

Urban Ecosystems - Although amphibians use human-created habitats in urban landscapes, few studies have investigated the quality of these habitats. To assess habitat quality of stormwater...     

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.     

Ebenezer Obadare: <Emphasis Type="Italic">Humor,Silence and Civil Society in Nigeria</Emphasis>

This is a book review of Ebenezer Obadare’s “Humor, Silence and Civil Society in Nigeria.” It examines conceptual issues around the role of civil society in Nigeria, the nexus (or lack thereof) between ngo’s and civil society, and the roles that humor and silence play as roles of agency in how a population responds to oppression and the suppression of dissent.     

A NEW CLASS OF MODELS FOR THE DISCRIMINANT PROBLEM

Discriminant analysis is relevant to business decision making in a variety of contexts, such as when one decides to make or buy a specified component, fund a venture project, or hire a particular person. Potential applications in artificial intelligence, particularly in the area of pattern recognition, have further underscored the importance of the field. A recent innovation in discriminant analysis is provided by special linear programming (LP) models, which offer attractive alternatives to classical statistical approaches. The scope of application in which discriminant analysis can be advantageously employed is broadened by the flexibility to tailor parameters in the LP approaches to reflect diverse goals and by the power to explore the sensitivity of these parameters. In spite of the promise of the LP formulations, however, limitations to their effectiveness have been uncovered in certain settings. A recent advance involving a normalization construct removes some of the limitations but entails solving the LP model twice (to allow for different signs of a normalization constant) and does not yield equivalent solutions for different rotations of the problem data. This paper introduces a new model and a new class of normalizations that remedy both remaining limitations, making it possible to take advantage of the modeling capabilities of the LP formulations without the attendant shortcomings encountered by earlier investigations. Our development shows by empirical testing and illustrative analysis that the quality of solutions from LP discriminant approaches is more favorable (relative to the classical model) than previously supposed.     

Is Chinese National Health Insurance Effective in the Face of Severe Illness? A Perspective from Health Service Utilization and Economic Burden

Over the past decade, health insurance coverage has expanded dramatically in China. Health insurance benefits, however, remain shallow or ambiguous. This study examines the effect of Chinese national health insurance policy on health service utilization and economic burden in urban settings using the Urban Resident Basic Medical Insurance Household Survey. We employ the Heckman selection model to correct for selection bias among hospital inpatients, and find that compared to individuals without health insurance, those with health insurance are more likely to be admitted to the hospital when their physicians recommended them to enter the hospital as inpatients. Health insurance is also associated with about 45.6 % decrease in out-of-pocket inpatient expenditures. Individuals with urban employee basic medical insurance see the largest decrease in economic burden, followed by individuals with urban resident basic medical Insurance, and those with new rural cooperative medical insurance.