Independent attributes control chart for Markov-dependent processes

Statistical process monitoring (SPM) is a very efficient tool to maintain and to improve the quality of a product. In many industrial processes, end product has two or more attribute-type quality characteristics. Some of them are independent, but the observations are Markovian dependent. It is essential to develop a control chart for such situations. In this article, we develop an Independent Attributes Control Chart for Markov Dependent Processes based on error probabilities criterion under the assumption of one-step Markov dependency. Implementation of the chart is similar to that of Shewhart-type chart. Performance of the chart has been studied using probability of detecting shift criterion. A procedure to identify the attribute(s) responsible for out-of-control status of the process is given.     

A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy

When elections are close in time, voters may stick to their preferred party or chose a different option for several reasons; reliable estimates of the amount of transitions across the available options can allow to answer a number of relevant questions about electoral behaviour. We describe a modified version of the model due to Brown and Payne (J Am Stat Assoc 81:453–460, 1986) and argue that it is based on simple, yet realistic, assumptions with a direct interpretation in terms of individual behaviour and compares well with other models proposed more recently. We apply the model to an Italian borough where, during June 2009, two elections were held simultaneously and a runoff took place two weeks later. Estimates of the joint distribution of voters between the European Parliament election and the other two elections provide evidence of substantially different kinds of voting behaviour which, given the specific context, we interpret in the light of the recent literature on the subject.     

A Comparative Pharmacokinetic Estimate of Mercury in U.S. Infants Following Yearly Exposures to Inactivated Influenza Vaccines Containing Thimerosal

The use of thimerosal preservative in childhood vaccines has been largely eliminated over the past decade in the United States because vaccines have been reformulated in single‐dose vials that do not require preservative. An exception is the inactivated influenza vaccines, which are formulated in both multidose vials requiring preservative and preservative‐free single‐dose vials. As part of an ongoing evaluation by USFDA of the safety of biologics throughout their lifecycle, the infant body burden of mercury following scheduled exposures to thimerosal preservative in inactivated influenza vaccines in the United States was estimated and compared to the infant body burden of mercury following daily exposures to dietary methylmercury at the reference dose established by the USEPA. Body burdens were estimated using kinetic parameters derived from experiments conducted in infant monkeys that were exposed episodically to thimerosal or MeHg at identical doses. We found that the body burden of mercury (AUC) in infants (including low birth weight) over the first 4.5 years of life following yearly exposures to thimerosal was two orders of magnitude lower than that estimated for exposures to the lowest regulatory threshold for MeHg over the same time period. In addition, peak body burdens of mercury following episodic exposures to thimerosal in this worst‐case analysis did not exceed the corresponding safe body burden of mercury from methylmercury at any time, even for low‐birth‐weight infants. Our pharmacokinetic analysis supports the acknowledged safety of thimerosal when used as a preservative at current levels in certain multidose infant vaccines in the United States.     

Household time activities,food waste,and diet quality: the impact of non-marginal changes due to COVID-19

Review of Economics of the Household - In early 2020, a novel coronavirus quickly spread across the globe. In response to the rapidly increasing number of confirmed U.S. cases, state and local...     

Building Health Insurance Literacy: Evidence from the Smart Choice Health Insurance™ Program

With the passage of the Patient Protection and Affordable Care Act (ACA) of 2010, the health insurance literacy of Americans became a critical issue. In response, a consumer education program was created and tested by university researchers and educators associated with Cooperative Extension. This article draws extensively on the emerging literature on health insurance literacy and on data from participants in the Smart Choice Health Insurance? program. The intent of the study was to understand socio-demographic and environmental variables that predict initial health insurance literacy and gains in health insurance literacy. A standardized instrument measuring health insurance literacy was used to collect the data. Multivariate analysis showed higher income consumers demonstrated greater initial health insurance literacy scores compared to middle income consumers, whereas younger, male and lower educated consumers reported lower initial health insurance literacy. After participating in the Smart Choice Health Insurance? program, consumers who made greater gains in their health insurance literacy tended to be female, higher income, and consumers residing in states that showed supportiveness of the ACA. The findings highlight the importance of considering sociodemographic characteristics in program design and delivery, as well as how contextual issues, such as the political environment, might impact the delivery of educational efforts. Findings from the analyses help inform ways to adapt and tailor educational opportunities that focus on health insurance literacy for a range of consumers.     

The Ethical Challenges of Socially Responsible Science

Social responsibility is an essential part of the responsible conduct of research that presents difficult ethical questions for scientists. Recognizing one’s social responsibilities as a scientist is an important first step toward exercising social responsibility, but it is only the beginning, since scientists may confront difficult value questions when deciding how to act responsibly. Ethical dilemmas related to socially responsible science fall into at least three basic categories: 1) dilemmas related to problem selection, 2) dilemmas related to publication and data sharing, and 3) dilemmas related to engaging society. In responding to these dilemmas, scientists must decide how to balance their social responsibilities against other professional commitments and how to avoid compromising their objectivity. In this article, we will examine the philosophical and ethical basis of social responsibility in science, discuss some of the ethical dilemmas related to exercising social responsibility, and make five recommendations to help scientists deal with these issues.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

The Box–Cox Transformation and Non‐Iterative Estimation Methods for Ordinal Log‐Linear Models

Recently Beh and Farver investigated and evaluated three non‐iterative procedures for estimating the linear‐by‐linear parameter of an ordinal log‐linear model. The study demonstrated that these non‐iterative techniques provide estimates that are, for most types of contingency tables, statistically indistinguishable from estimates from Newton's unidimensional algorithm. Here we show how two of these techniques are related using the Box–Cox transformation. We also show that by using this transformation, accurate non‐iterative estimates are achievable even when a contingency table contains sampling zeros.