Measuring compulsive buying behaviour: Psychometric validity of three different scales and prevalence in the general population and in shopping centres

Due to the problems of measurement and the lack of nationally representative data, the extent of compulsive buying behaviour (CBB) is relatively unknown. Methods: The validity of three different instruments was tested: Edwards Compulsive Buying Scale (ECBS; Edwards, 1993), Questionnaire About Buying Behavior (QABB; Lejoyeux & Adès, 1994) and Richmond Compulsive Buying Scale (RCBS; Ridgway, et. al., 2008) using two independent samples. One was nationally representative of the Hungarian population (N=2710) while the other comprised shopping mall customers (N=1447). Results: A new, four-factor solution for the ECBS was developed (ECBS-R), and confirmed the other two measures. Additionally, cut-off scores were defined for all measures. Results showed that the prevalence of CBB is 1.85% (with QABB) in the general population but significantly higher in shopping mall customers (8.7% with ECBS-R, 13.3% with QABB and 2.5% with RCBS-R). Conclusions: Due to the diversity of content, each measure identifies a somewhat different CBB group

Non Parametric Confidence Intervals for Receiver Operating Characteristic Curves

We study methods for constructing confidence intervals, and confidence bands, for estimators of receiver operating characteristics. Particular emphasis is placed on the way in which smoothing should be implemented, when estimating either the characteristic itself or its variance. We show that substantial undersmoothing is necessary if coverage properties are not to be impaired. A theoretical analysis of the problem suggests an empirical, plug-in rule for bandwidth choice, optimising the coverage accuracy of interval estimators. The performance of this approach is explored. Our preferred technique is based on asymptotic approximation, rather than a more sophisticated approach using the bootstrap, since the latter requires a multiplicity of smoothing parameters all of which must be chosen in nonstandard ways. It is shown that the asymptotic method can give very good performance.Bandwidth selection, binary classification, kernel estimator, receiver operating characteristic curve.

The Predictive Validity of the Early Warning System Tool

The Early Warning System (EWS) is a tool developed by the National High School Center to collect data on indicators including attendance, GPA, course failures and credits earned. These indicators have been found to be highly predictive of a student’s likelihood of dropping out of high school in large, urban areas. The EWS tool was studied in two suburban schools. With the exception of attendance data, findings suggest that the indicators and suggested threshold for risk determination are predictive in suburban contexts

Automatic Detection of Slip-Induced Backward Falls

Falls are the leading cause of injury deaths among people 65 years and older. The National Safety Council reported that in 2005, 17,700 Americans met their death by falling, and of these deaths, the majority (over 80%) were people over 65 years of age [1]. It is certainly desirable to avoid the fall accidents altogether through developing a comprehensive fall prevention program [2]. However, in case of unavoidable falls, an effective injury-prevention technology is critical to minimize/reduce fall-related physical injuries. Recently, the concept of wearable airbag [3] emerged as one viable and promising injury-prevention approach

Отбор переменных в логистическую регрессию генетическим алгоритмом

В статье исследуются эффективные процедуры отбора переменных в бинарные классифицирующие модели на основе логистической регрессии. Для этого используется генетический алгоритм, причем в функцию фитнеса особи параметр штрафа за включение в модель новых переменных изменяется в зависимости от рассчитанного значения площади под ROC-кривой. Проведены эксперименты на модельных наборах данных и в задаче кредитного скоринга.У статті досліджуються ефективні процедури відбору змінних в бінарні класифікуючі моделі на основі логістичної регресії. Для цього використовується генетичний алгоритм, причому у функцію фітнеса особини параметр штрафу за включення в модель нових змінних змінюється залежно від розрахованого значення площі під ROC-кривою. Проведені експерименти на модельних наборах даних і в задачі кредитного скорингу.In the paper we discuss effective procedures for а feature selection problem in a binary logistic regression model. A genetic algorithm was used to find best feature combinations, with the special fitness function based on a penalty parameter for including new variables. This parameter depends on ROC-curve index on current epoch. Experiments on Madelon data set and credit scoring classification problem were made

Robustness of approaches to ROC curve modeling under misspecification of the underlying probability model

The receiver operating characteristic (ROC) curve is a tool of particular use in disease status classification with a continuous medical test (marker). A variety of statistical regression models have been proposed for the comparison of ROC curves for different markers across covariate groups. A full parametric modeling of the marker distribution has been generally found to be overly reliant on the strong parametric assumptions. Pepe (2003) has instead developed parametric models for the ROC curve that induce a semi-parametric model for the marker distributions. The estimating equations proposed for use in these ROC-GLM models may differ from commonly used estimating equations in those same probability models. In this paper, we investigate the analysis of the power ROC curve when based on the parametric exponential model and the broader semi-parametric proportional hazards probability model. In the case of the latter, we consider estimating equations derived from the usual partial likelihood methods in time-to-event analyses and the ROC-GLM approach of Pepe, et al. In exploring the robustness of these ROC analysis approaches to violations of the distributional assumptions, we find that the ROC-GLM estimating equation provides an extra measure of robustness when compared to the Cox proportional hazards estimating equation

Presenting a simplified assistant tool for breast cancer diagnosis in mammography to radiologists

This paper proposes a method to simplify a computational model from logistic regression for clinical use without computer. The model was built using human interpreted featrues including some BI-RADS standardized features for diagnosing the malignant masses. It was compared with the diagnosis using only assessment categorization from BI-RADS. The research aims at assisting radiologists to diagnose the malignancy of breast cancer in a way without using automated computer aided diagnosis system

Development and Validation of Spatially Explicit Habitat Models for Cavity-nesting Birds in Fishlake National Forest, Utah

The ability of USDA Forest Service Forest Inventory and analysis (FIA) generated spatial products to increase the predictive accuracy of spatially explicit, macroscale habitat models was examined for nest-site selection by cavity-nesting birds in Fishlake National Forest, Utah. One FIA-derived variable (percent basal area of aspen trees) was significant in the habitat model; however, the incorporation of FIA stand structure information did not increase model accuracy. Cavity-nesting birds respond strongly to nest-tree attributes unable to be modeled spatially for this study. Future modeling efforts should focus on larger taxa (e.g., ungulates) and richness/diversity studies