Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.     

Using relative utility curves to evaluate risk prediction

Summary.  Because many medical decisions are based on risk prediction models that are constructed from medical history and results of tests, the evaluation of these prediction models is important. This paper makes five contributions to this evaluation: the relative utility curve which gauges the potential for better prediction in terms of utilities, without the need for a reference level for one utility, while providing a sensitivity analysis for misspecification of utilities, the relevant region, which is the set of values of prediction performance that are consistent with the recommended treatment status in the absence of prediction, the test threshold, which is the minimum number of tests that would be traded for a true positive prediction in order for the expected utility to be non-negative, the evaluation of two-stage predictions that reduce test costs and connections between various measures of performance of prediction. An application involving the risk of cardiovascular disease is discussed.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

New V control chart for the Maxwell distribution

Control charts have been popularly used as a user-friendly yet technically sophisticated tool to monitor whether a process is in statistical control or not. These charts are basically constructed under the normality assumption. But in many practical situations in real life this normality assumption may be violated. One such non-normal situation is to monitor the process variability from a skewed parent distribution where we propose the use of a Maxwell control chart. We introduce a pivotal quantity for the scale parameter of the Maxwell distribution which follows a gamma distribution. Probability limits and L-sigma limits are studied along with performance measure based on average run length and power curve. To avoid the complexity of future calculations for practitioners, factors for constructing control chart for monitoring the Maxwell parameter are given for different sample sizes and for different false alarm rate. We also provide simulated data to illustrate the Maxwell control chart. Finally, a real life example has been given to show the importance of such a control chart.     

Estimation of flood frequencies from data sets with outliers using mixed distribution functions

In this paper the estimation of high return period quantiles of the flood peak and volume in the Kolubara River basin are carried out. Estimation of flood frequencies is carried out on a data set containing high outliers which are identified by the Rosner’s test. Simultaneously, low outliers are determined by the multiple Grubbs–Beck. The next step involved the usage of the mixed distribution functions applied to a data set from three populations: floods with low outliers, normal floods and floods with high outliers. The contribution of the data set with low outliers is neglected, since it should underestimate the flood quantiles with large return periods. Consequently, the best fitted mixed distribution from the applied types (EV1, GEV, P3 and LP3) was determined by using the minimum standard error of fit.     

Letter to the editor comments on Groparu-Cojocaru and Doray (2013)

Although estimating the five parameters of an unknown Generalized Normal Laplace (GNL) density by minimizing the distance between the empirical and true characteristic functions seems appealing, the approach cannot be advocated in practice. This conclusion is based on extensive numerical simulations in which a fast minimization procedure delivers deceiving estimators with values that are quite far away from the truth. These findings can be predicted by the very large values obtained for the true asymptotic variances of the estimators of the five parameters of the true GNL density.     

Designing a multiple state repetitive group sampling plan based on the coefficient of variation

A multiple state repetitive group sampling (MSRGS) plan is developed on the basis of the coefficient of variation (CV) of the quality characteristic which follows a normal distribution with unknown mean and variance. The optimal plan parameters of the proposed plan are solved by a nonlinear optimization model, which satisfies the given producer's risk and consumer's risk at the same time and minimizes the average sample number required for inspection. The advantages of the proposed MSRGS plan over the existing sampling plans are discussed. Finally an example is given to illustrate the proposed plan.     

Developmental Patterns in Marital Satisfaction: Another Look at Covenant Marriage

This study investigated differences in the trajectory of marital satisfaction in the first 7 years between couples in covenant versus standard marriages. The authors analyzed data on 707 Louisiana marriages from the Marriage Matters Panel Survey of Newlywed Couples, 1998–2004, using multivariate longitudinal growth modeling. When the sample was restricted to couples who remained married over the duration of the study, a marginal benefit of covenant status was found for husbands. This effect was largely accounted for by covenant husbands' more extensive exposure to premarital counseling. The linear decline in marital satisfaction over time that obtained for both husbands and wives was not, however, any different for covenant marriages versus standard marriages. Couples characterized by more traditional attitudes toward gender roles were significantly less satisfied than others. High premarital risk factors, initial uncertainty about marrying the spouse, and the presence of preschool‐age children in the household were all corrosive of marital satisfaction at any given time.     

Correlates of perceived effectiveness of the Safe Schools/Healthy Students Initiative

A three-level growth-curve model was applied to estimate perceived impact growth trajectories, using multi-year data from project and school surveys on outcome and program implementation collected from 59 sites and approximately 1165 participating schools in the Safe Schools and Healthy Students Initiative. Primary interest is to determine whether and how project-level and school-level correlates affect schools’ perceptions of the Initiative's effectiveness over time when the effects of the pre-grant environmental conditions, grant operations, and near-term outcomes are considered. Coordination and service integration, comprehensive programs and activities for early childhood development, and change in school involvement were found to be significant predictors of school-perceived overall impact when the effect of poverty was considered. Partnership functioning, perceived importance of school resources, and school involvement were found to be significant predictors of school-perceived impact on substance use prevention when the effect of poverty was considered.     

The Pursuit of Continuous Improvement in the Field of Intergenerational Relationships: The Discipline of the Second Curve

This article presents a case for the field of intergenerational relationships to embrace the challenges associated with progressing its reputation, boundaries, and potential in the twenty-first century. The concept of the Sigmoid curve based upon curvilinear logic and the discipline of the second curve is introduced as a means of understanding how individuals, organizations, and institutions need to deal with the challenge of constant renewal as a consequence of rapid social change. The field of intergenerational relationships is challenged to align itself with the discipline of the second curve by (1) improving the impact of research through knowledge-based transfer and (2) exploring new fields of inquiry in the interest of maintaining and advancing relevance and legitimacy.