Protecting the Consumer Through Truth-in-Dieting Laws

Although diets are ineffective in producing sustained weight loss, the multibillion dollar weight loss industry continues to make exaggerated claims of efficacy without substantiating outcome data. Lacking accurate information, individuals continue to pay for ineffective weight loss programs and products. In this article the author describes her application of social science data to influence state policy, which led to the passage of two first-in-the-nation diet disclosure laws. The 1996 law mandates that dieting companies make advertising claims only based on outcome data. The 1997 law requires full disclosure of program costs, conditions, and credentials of staff, and grants consumers a 3-day cancellation right. Together, the diet disclosure laws prohibit the use of deceptive practices and hold the diet industry accountable by requiring that all claims be based on representative group data.     

Robustness of Approaches to ROC Curve Modeling under Misspecification of the Underlying Probability Model

A variety of statistical regression models have been proposed for the comparison of ROC curves for different markers across covariate groups. Pepe developed parametric models for the ROC curve that induce a semiparametric model for the market distributions to relax the strong assumptions in fully parametric models. We investigate the analysis of the power ROC curve using these ROC-GLM models compared to the parametric exponential model and the estimating equations derived from the usual partial likelihood methods in time-to-event analyses. In exploring the robustness to violations of distributional assumptions, we find that the ROC-GLM provides an extra measure of robustness.     

Equilibrium probabilities for a production-inventory system maintained by a control-limit policy

Abstract

In this paper we consider a manufacturing system in which an input generator supplies at a constant rate a buffer with a raw material and a production unit pulls the raw material from the buffer at a stochastic rate. We consider specific control-limit policies for the maintenance of the production unit. We show that the equilibrium probabilities of the system can be computed exactly when the production unit after a maintenance remains idle until the buffer is filled up.     

Influential observation in complex normal data for problems in allometry

Abstract

The aim of this paper is to investigate how some results related to the complex normal distribution are relevant in size and shape analysis. Our main focus is on the derivation of influential measures. In particular, Cook and Kullback–Leibler distances are combined with their respective asymptotic results as well as to an alternative process of defining cut-off points. Some numerical examples illustrate how these measures are used in practice. We perform an application to simulated and actual data. Results provide evidence that the methodology based on Kullback–Leibler distance outperforms one in terms of the Cook classic distance.     

The exponential Poisson logarithmic distribution

Abstract

In this article, we proposed a new three parameter lifetime distribution motivated mainly by lifetime issues, which generalizes the Exponential Poisson distribution proposed by Cancho et al. (2011) Cancho, V.G., Louzada-Neto, F., Barriga, G.D. (2011). The poisson-exponential lifetime distribution. Computat. Statist. Data Anal. 55:677–686.[Crossref], [Web of Science ®] , [Google Scholar]. We derive various standard mathematical properties of the proposed model including a formal proof of its probability density function and hazard rate function. The inference via the maximum likelihood approach is discussed. The performance of the maximum likelihood estimators, the likelihood ratio test and its power are studied by simulation. Finally, the proposed model is fitted to two real data sets and it is compared with several models.     

On the use of several auxiliary variables in estimation of current population mean in successive sampling

ABSTRACT

The present work intends to put emphasis on the role of several auxiliary variables on both the occasions to improve the precision of estimates at current occasion in two-occasion successive sampling. Utilizing the readily available information on several auxiliary variables on both occasions and the information on study variable from the previous occasion, an efficient estimation procedure of population mean on current occasion has been suggested. Optimum replacement strategy and the efficiencies of the proposed estimator have been discussed. Empirical studies are carried out, and appropriate recommendations have been put forward for practical applications.     

THE HARMS SHOE COMPANY: DEVELOPMENT OF A COMPUTER MODEL FOR THE CLASSROOM

This paper describes a case that examines five key topics in production and operations management [1]—forecasting, inventory control, transportation planning, aggregate planning, and the disaggregation of the aggregate plan—in an integrated, realistic manner for upper-level undergraduate business majors [3]. The case structure stresses the linkage of these interrelated subjects and supplements the regular classroom presentations dealing with them. The case relies on computer calculations at each stage to provide the information input for calculations at the next stage. It is a static model with a twelve-month planning horizon. Students complete several exercises and assume several roles as the case unfolds. They act on their own in filling some roles and as members of teams in performing others. They do not compete with each other, as the purpose of the case is to help them develop an appreciation of the factors that persons performing the role must consider.     

Choosing the Number of Instruments

Properties of instrumental variable estimators are sensitive to the choice of valid instruments, even in large cross‐section applications. In this paper we address this problem by deriving simple mean‐square error criteria that can be minimized to choose the instrument set. We develop these criteria for two‐stage least squares (2SLS), limited information maximum likelihood (LIML), and a bias adjusted version of 2SLS (B2SLS). We give a theoretical derivation of the mean‐square error and show optimality. In Monte Carlo experiments we find that the instrument choice generally yields an improvement in performance. Also, in the Angrist and Krueger (1991) returns to education application, when the instrument set is chosen in the way we consider, it turns out that both 2SLS and LIML give similar (large) returns to education.