Patterns of Reactive and Proactive Aggression in Young Adolescents in Singapore

The authors investigated the patterns of reactive and proactive aggression exhibited by young male (N = 604) and female (N = 544) adolescents in Singapore. Self‐report measures of reactive and proactive aggression, behavioral and emotional adjustment, parenting styles, and delinquency were administered to students aged 13–14. Using cluster analysis, three distinct patterns of aggression emerged: a low aggressive group, a combined aggressive group with high reactive and proactive aggression, and a reactively aggressive group with low proactive and high reactive aggression. The two aggressive groups showed similar disturbances in adjustment and delinquency, but the combined group showed the greatest disruptions. Findings indicate cross‐cultural stability of patterns of aggression as well as the usefulness of the reactive/proactive distinction in early identification of individuals with adjustment problems.     

Control charts for generalized exponential distribution percentiles

The generalized exponential (GE) distribution, which was introduced by Mudholkar and Srivastava in 1993 Mudholkar, G. S., Srivastava, D. K. (1993). Exponentiated Weibull family for analyzing bathtub failure data. IEEE Transactions on Reliability 42:299–302. [Google Scholar], has been studied for various applications of lifetime modelings. In this article, five control charts, that comprise the Shewhart-type chart and four parametric bootstrap charts based on maximum likelihood estimation method, the moment estimation method, probability plot method, and least-square error method for the GE percentiles, are investigated. An extensive Monte Carlo simulation study is conducted to compare the performance among all five control charts in terms of average run length. Finally, an example is given for illustration.     

Nonparametric methodology for the time-dependent partial area under the ROC curve

To assess the classification accuracy of a continuous diagnostic result, the receiver operating characteristic (ROC) curve is commonly used in applications. The partial area under the ROC curve (pAUC) is one of the widely accepted summary measures due to its generality and ease of probability interpretation. In the field of life science, a direct extension of the pAUC into the time-to-event setting can be used to measure the usefulness of a biomarker for disease detection over time. Without using a trapezoidal rule, we propose nonparametric estimators, which are easily computed and have closed-form expressions, for the time-dependent pAUC. The asymptotic Gaussian processes of the estimators are established and the estimated variance-covariance functions are provided, which are essential in the construction of confidence intervals. The finite sample performance of the proposed inference procedures are investigated through a series of simulations. Our method is further applied to evaluate the classification ability of CD4 cell counts on patient's survival time in the AIDS Clinical Trials Group (ACTG) 175 study. In addition, the inferences can be generalized to compare the time-dependent pAUCs between patients received the prior antiretroviral therapy and those without it.     

Liquidity planning in a small bank

Given a forecast of supply and demand for cash in each period of an infinite planning horizon, and with a known current portfolio, a policy is chosen to invest these cash supplies in securities of different maturities so that the demand in every future period can be satisfied by securities maturing in that period. The objective is to maximize the minimum of the excess over the planning horizon so that any illiquidity in one period is spread out over the entire planning horizon. Analytical solutions are obtained for single maturity and barbell investment policies. Feasibility and optimality conditions are determined for these policies.     

A linear formulation of the two-level DEA model

In efficiency measurement, the input and output factors of similar characteristics can be grouped into input and output categories, respectively, using a weighted-average approach under the data envelopment analysis (DEA) framework to form a two-level DEA model. The resulting two-level model is nonlinear. This note transforms the nonlinear model into a linear one using a variable substitution technique. Linear models are easier to solve than their nonlinear counterparts. The linear transformation is applicable to the two-level model built from both the primal and dual forms of the conventional one-level DEA model. The linear model transformed from the nonlinear model developed from the primal form has a dual which is equivalent to the nonlinear model developed from the dual form.     

Efficiency analysis of university departments: An empirical study

Universities play an important role in the development of a country in this age of the knowledge economy. As government subsidies to universities have been reducing in recent years, the more efficient use of resources becomes an important issue for university administrators. This paper applies data envelopment analysis (DEA) to assess the relative efficiency of the academic departments at National Cheng Kung University in Taiwan. The outputs considered are total credit-hours, publications, and external grants; and the inputs utilized by the departments are personnel, operating expenses, and floor space. An assurance region is constructed by the top administrators of the university to confine the flexibility in selecting the virtual multipliers in DEA. Four groups of departments of similar characteristics are categorized via an efficiency decomposition and cluster analysis. The aggregate efficiency indicates whether the resources have been utilized efficiently by a department and the efficiency decomposition helps identify the weak areas where more effort should be devoted so that the efficiency of the department can be improved.     

Estimation methods for time‐dependent AUC models with survival data

The performance of clinical tests for disease screening is often evaluated using the area under the receiver‐operating characteristic (ROC) curve (AUC). Recent developments have extended the traditional setting to the AUC with binary time‐varying failure status. Without considering covariates, our first theme is to propose a simple and easily computed nonparametric estimator for the time‐dependent AUC. Moreover, we use generalized linear models with time‐varying coefficients to characterize the time‐dependent AUC as a function of covariate values. The corresponding estimation procedures are proposed to estimate the parameter functions of interest. The derived limiting Gaussian processes and the estimated asymptotic variances enable us to construct the approximated confidence regions for the AUCs. The finite sample properties of our proposed estimators and inference procedures are examined through extensive simulations. An analysis of the AIDS Clinical Trials Group (ACTG) 175 data is further presented to show the applicability of the proposed methods. The Canadian Journal of Statistics 38:8–26; 2010 © 2009 Statistical Society of Canada     

Non‐parametric estimation for time-dependent AUC

The area under the receiver operating characteristic (ROC) curve (AUC) is one of the commonly used measure to evaluate or compare the predictive ability of markers to the disease status. Motivated by an angiographic coronary artery disease (CAD) study, our objective is mainly to evaluate and compare the performance of several baseline plasma levels in the prediction of CAD-related vital status over time. Based on censored survival data, the non-parametric estimators are proposed for the time-dependent AUC. The limiting Gaussian processes of the estimators and the estimated asymptotic variance–covariance functions enable us to further construct confidence bands and develop testing procedures. Applications and finite sample properties of the proposed estimation methods and inference procedures are demonstrated through the CAD-related death data from the British Columbia Vital Statistics Agency and Monte Carlo simulations.