Estimation and inference of the joint conditional distribution for multivariate longitudinal data using nonparametric copulas

In this paper we study estimating the joint conditional distributions of multivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models, we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Empirical copulas combined with time-varying transformation models may allow quite flexible modelling for the joint conditional distributions for multivariate longitudinal data. We derive the asymptotic properties for the copula-based estimators of the joint conditional distribution functions. For illustration we apply our estimation method to an epidemiological study of childhood growth and blood pressure.     

Order consolidation for hierarchical product lines

We consider the batch production of hierarchical product lines in raw material industry where the whole or parts of multiple customer orders may be consolidated and processed in the same batch if their product specifications are compatible. The objective of the problem is to find maximum possible number of batches completely filled up to their capacity. The compatibility relationship among product specifications is represented by a graph called the compatibility graph. If the compatibility graph is an arbitrary graph, the problem is proven to be NP-hard and belongs to Max SNP-hard class. We develop an optimum algorithm for an important subclass of the problem where the graph is a quasi-threshold graph which in fact is the case for producing hierarchical product lines that are often found in raw materials industry.     

Relationship of sleep quality and attention deficit hyperactivity disorder symptoms with quality of life in college students

Abstract

Objective: Attention deficit hyperactivity disorder (ADHD) negatively influences various aspects of life such as social relations, adaptive skills, and occupation. In addition, many university students experience sleep problems, academic failure, and low quality of life (QOL). We investigated the relationship among ADHD symptoms, sleep quality, and QOL of college students, and identified the factors related to their QOL. Participants and Methods: Using a survey questionnaire, data were collected from 195 students from March–May 2017. Results: QOL of students was related to ADHD symptoms and sleep quality. Factors significantly associated with better QOL were fewer ADHD symptoms, good sleep quality, male sex, high academic performance, and high economic status. The explanatory power of these variables on QOL was 32.0%. Conclusion: To improve the QOL of college students, a multifaceted approach that includes assessment of ADHD symptoms and sleep quality is needed.     

An evaluation of the bootstrap for model validation in mixture models

Bootstrapping has been used as a diagnostic tool for validating model results for a wide array of statistical models. Here we evaluate the use of the non-parametric bootstrap for model validation in mixture models. We show that the bootstrap is problematic for validating the results of class enumeration and demonstrating the stability of parameter estimates in both finite mixture and regression mixture models. In only 44% of simulations did bootstrapping detect the correct number of classes in at least 90% of the bootstrap samples for a finite mixture model without any model violations. For regression mixture models and cases with violated model assumptions, the performance was even worse. Consequently, we cannot recommend the non-parametric bootstrap for validating mixture models.

The cause of the problem is that when resampling is used influential individual observations have a high likelihood of being sampled many times. The presence of multiple replications of even moderately extreme observations is shown to lead to additional latent classes being extracted. To verify that these replications cause the problems we show that leave-k-out cross-validation where sub-samples taken without replacement does not suffer from the same problem.     

Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach

Conducting an early warning forecast to detect potential cost overrun is essential for timely and effective decision-making in project control. This paper presents a forecast combination model that adaptively identifies the best forecast and optimises various combinations of commonly used project cost forecasting models. To do so, a forecast error simulator is formulated to visualise and quantify likely error profiles of forecast models and their combinations. The adaptive cost combination (ACC) model was applied to a pilot project for numerical illustration as well as to real world projects for practical implementation. The results provide three valuable insights into more effective project control and forecasting. First, the best forecasting model may change in individual projects according to the project progress and the management priority (i.e. accuracy, outperformance or large errors). Second, adaptive combination of simple, index-based forecasts tends to improve forecast accuracy, while mitigating the risk of large errors. Third, a post-mortem analysis of seven real projects indicated that the simple average of two most commonly used cost forecasts can be 31.2% more accurate, on average, than the most accurate alternative forecasts.     

Medicare Privatization and the Erosion of Retirement Security

This paper describes initiatives to privatize the Medicare program over the last 10 years and the implications of these initiatives for the future of retirement security. Our analysis focuses on the privatization provisions of the Medicare Modernization Act, which is largely designed to benefit the corporate health care sector without containing costs or significantly reducing the threat of rising health care costs to the economic security of current and future retirees. In fact, as designed, the Medicare Modernization Act is likely to increase the threat to retirement security in the years ahead. We conclude with a series of policy alternatives to the neoliberal agenda for the privatization of Medicare.