SPARSE LOGISTIC PRINCIPAL COMPONENTS ANALYSIS FOR BINARY DATA

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.     

Autoregressive model selection based on a prediction perspective

The autoregressive (AR) model is a popular method for fitting and prediction in analyzing time-dependent data, where selecting an accurate model among considered orders is a crucial issue. Two commonly used selection criteria are the Akaike information criterion and the Bayesian information criterion. However, the two criteria are known to suffer potential problems regarding overfit and underfit, respectively. Therefore, using them would perform well in some situations, but poorly in others. In this paper, we propose a new criterion in terms of the prediction perspective based on the concept of generalized degrees of freedom for AR model selection. We derive an approximately unbiased estimator of mean-squared prediction errors based on a data perturbation technique for selecting the order parameter, where the estimation uncertainty involved in a modeling procedure is considered. Some numerical experiments are performed to illustrate the superiority of the proposed method over some commonly used order selection criteria. Finally, the methodology is applied to a real data example to predict the weekly rate of return on the stock price of Taiwan Semiconductor Manufacturing Company and the results indicate that the proposed method is satisfactory.     

Cases for the nugget in modeling computer experiments

Most surrogate models for computer experiments are interpolators, and the most common interpolator is a Gaussian process (GP) that deliberately omits a small-scale (measurement) error term called the nugget. The explanation is that computer experiments are, by definition, “deterministic”, and so there is no measurement error. We think this is too narrow a focus for a computer experiment and a statistically inefficient way to model them. We show that estimating a (non-zero) nugget can lead to surrogate models with better statistical properties, such as predictive accuracy and coverage, in a variety of common situations.     

Developing Independence: the experience of the Lawnmowers Theatre Company

This paper describes an action research project in which the Lawnmowers Theatre Company (a company of performers with learning disabilities) mounted drama clubs and a series of night clubs for people with learning disabilities. The objective of the project was to demonstrate that initiatives involving what might loosely be called 'drama' offer important opportunities for adult education and that night clubs for people with learning disabilities can be effective in providing new opportunities for social contact and also recognition for disability arts. All of these factors, it is argued, are important if people with learning disabilities are to be taken seriously as capable, interesting individuals with something useful to say. The project found high levels of participation and a range of skills acquisition.     

Editorial

Lifetime Data Analysis -     

Development of a Performance Assessment Capability in the Waste Management Programs of the U.S. Nuclear Regulatory Commission

The U.S. Nuclear Regulatory Commission (NRC) staff has developed a performance assessment capability to address three programmatic areas in nuclear waste management: high-level waste, low-level waste, and decommissioning of licensed facilities (license termination). The NRC capability consists of: (1) methodologies for performance assessment; (2) models and computer codes for estimating system performance; (3) regulatory guidance in various forms, such as regulations, Branch Technical Positions, and Standard Review Plans; and (4) a technical staff experienced in executing and evaluating performance assessments for a variety of waste systems. Although the tools and techniques are refined for each programmatic area, general approaches and similar issues are encountered in all areas.     

Teaching graduate-level evaluation sociology through a departmental evaluation

The Department of Sociology at the University of Texas at Austin conducted an internal evaluation of its graduate program through an applied methodology graduate course. This article reports on the effectiveness of the format for teaching research methods, the utilization of the evaluation's findings, and the conditions that need to occur for a similar study to take place. Through the course, graduate students gained experience using multiple methods in evaluation research as well as acquiring an inside perspective on the department and the profession. Although some problems of conducting an internal evaluation are discussed, we found that with departmental support the evaluation was an effective method of promoting internal discourse and change.     

Effect of child benefit payments on child outcome expenditures in South Korea

Extensive research has shown the positive impact of conditional child benefits on child outcomes. However, there is limited work on the impact of universal child benefit payments on how families spend on child outcomes. Our study explores this issue. This study examined the relationship between child benefit payments on child outcome expenditures using longitudinal data from the Korean National Survey of Tax and Benefit (N = 3681 households) and a household- and year-fixed effects regression model. We found that child benefit payments are positively associated with child outcome expenditures across family income groups. Furthermore, the analysis results suggest that compared to high-income family groups, low- and middle-income family groups increase spending on child outcome expenditures in response to child benefit payments. Finally, the policy implications of this are discussed.     

A Model for Master Production Scheduling in Uncertain Environments

In uncertain environments, the master production schedule (MPS) is usually developed using a rolling schedule. When utilizing a rolling schedule, the MPS is replanned periodically and a portion of the MPS is frozen in each planning cycle. The cost performance of a rolling schedule depends on three decisions: the choice of the replanning interval (R), which determines how often the MPS should be replanned; the choice of the frozen interval (F), which determines how many periods the MPS should be frozen in each planning cycle; and the choice of the forecast window (T), which is the time interval over which the MPS is determined using newly updated forecast data. This paper uses an analytical approach to study the master production scheduling process in uncertain environments without capacity constraints, where the MPS is developed using a rolling schedule. It focuses on the choices of F, R, and T for the MPS. A conceptual framework that includes all important MPS time intervals is described. The effects of F, R, and T on system costs, which include the forecast error, MPS change, setup, and inventory holding costs, are also explored. Finally, a mathematical model for the MPS is presented. This model approximates the average system cost as a function of F, R, T, and several environmental factors. It can be used to estimate the associated system costs for any combination of F, R, and T.