DESIGNING INSPECTION STRATEGIES FOR UNCERTAIN ENVIRONMENTS

This paper considers the problems of designing inspection strategies for production systems terms in the presence of environmental uncertainty. The framework for determining information priorities to support inspection system planning is presented in the contaxt of a generic production system that encompasses the characteristics of many real-world serial production systems. The impact on the design decision of five key variables is considered: quality of producton processes, quality of inspection procedures, penalty cost for undetected defective units, relative cost of improving process vs. inspection, and shape of the cost functions for process and inspection enhancement. The framework for analysis involves varying factors over two or three orders of magnitude to determine optimal inspection strategies across a wide range of environments These results are used to compare design decisions made in the presence of environmental uncertainty using expected-opportunity-cost and minimization-of-maximum-opportunity-cost approaches. Design strategies are identified for situations ranging from complete lack of knowledge about the environment through increasing levels of information. Information-gathering priorities are established, and the impact on the design decision of this additional information is assayed.     

METHODS FOR ANALYZING EXPERIMENTS WITH MULTIPLE CRITERIA*

An approach to analyzing experimental data with multiple criteria is explained and demonstrated on data from a test of the effectiveness of two posters. As a supplement to traditional multivariate analysis of variance and covariance, the application of a step-down F test is advocated when an ordering of the criterion is meaningful, and an analysis of contrasts is recommended when such an ordering is not managerially relevant. The step-down procedure has the advantage of simultaneously testing an overall hypothesis and hypotheses on each criterion variable.     

Intergenerational Childhood Maltreatment in Persons with DSM-IV Pathological Gambling and Their First-Degree Relatives

This study investigates the characteristics of individuals with DSM-IV pathological gambling (PG) who experienced childhood maltreatment and rates of maltreatment occurring in their first-degree relatives (FDRs). 94 subjects with DSM-IV PG, 91 controls, and 312 FDRs were assessed for childhood maltreatment as part of a family study of PG. Maltreatment was evaluated using the Revised Childhood Experiences Questionnaire. The Family Assessment Device was used to evaluate the functionality of the PG subject’s (or control’s) family of origin. Data were analyzed using logistic regression by the method of generalized estimating equations. Rates of maltreatment were significantly higher in subjects with PG than controls (61 vs. 25 %, P < 0.001). Subjects with PG who experienced maltreatment were more likely to be female, had more severe PG symptoms, had co-occurring mood and anxiety disorders, and reported greater early family life dysfunction than those with PG who did not experience maltreatment. Rates of maltreatment were higher in FDRs of PG subjects than controls (41 vs. 24 %, P = .002). Rates in FDRs of individuals with PG who experienced maltreatment themselves were still higher that in FDRs of those with PG who did not experience maltreatment (50 vs. 28 %, P = .009). The former were also more likely to have anxiety disorders, substance use disorders, and suicide attempts. The results suggest that childhood maltreatment in persons with PG is common and intergenerational. Rates of maltreatment in FDRs of PG subjects are high, particularly among those who experienced abuse. The implications of the findings are discussed.     

A PRESENT VALUE FORMULATION OF THE CLASSICAL EOQ PROBLEM*

The usual analysis of the deterministic economic order quantity problem seeks to minimize the average cost of inventory ordering and holding costs per unit time. An alternative approach described in this paper examines the present value of discounted costs over an infinite horizon. Differences in the solutions and implications of errors using the two methodologies are discussed.     

Modeling Competing Infectious Pathogens from a Bayesian Perspective: Application to Influenza Studies with Incomplete Laboratory Results

In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often co-circulate with influenza and cause influenza-like illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among individuals. We propose a Bayesian competing-risk model for multiple co-circulating pathogens for inference on transmissibility and intervention efficacies under the assumption that missingness in the biological confirmation of the pathogen is ignorable. Simulation studies indicate a reasonable performance of the proposed model even if the number of potential pathogens is misspecified. They also show that a moderate amount of missing laboratory test results has only a small impact on inference about key parameters in the setting of close contact groups. Using the proposed model, we found that a non-pharmaceutical intervention is marginally protective against transmission of influenza A in a study conducted in elementary schools.     

Panel Tests of Okun's Law for Ten Industrial Countries

Okun's Law is one of the most enduring stylistic facts in macroeconomics. This article uses new developments in trend/cycle decomposition to test Okun's Law for a panel of ten industrial countries, finding that Okun's original estimate for the U.S. of three points of real GDP growth for each one percent reduction in the unemployment rate now averages just under two points of real GDP growth for the sample countries. Pooled estimates for Europe are smaller than estimates for the rest of the sample. Also, this article finds that omission of capital and labor inputs may have biased previous estimates.     

Measuring Gay Populations and Antigay Hate Crime

Objectives . The study of crime directed at gay and lesbian targets is hampered by two measurement problems: Police agencies provide unreliable data on hate crime, and tract-level census data contain no direct information about gay or lesbian population density. This article attempts to gauge two quantities that cannot be measured directly or unambiguously: the size of the gay and lesbian populations and the number of hate crimes directed at gay and lesbian targets. Methods . Population data for New York City were gathered from market research lists and from a special tabulation of the 1990 Census. Hate crime data were obtained from the Anti-Violence Project and the New York Police Department. Confirmatory factor analysis was used to assess the reliability of each measure and the correlation between latent population density and hate crime. Results . Each of these measures offers a reliable means by which to assess cross-sectional differences in the population density and victimization of gay men. Census and police data prove to be inferior indicators of lesbian population density and antilesbian hate crime. For both men and women, population density is strongly correlated with the incidence of hate crime. Conclusions . Despite the fact that advocacy groups record many more antigay incidents than do the police, both sources of data are in agreement about where hate crimes occur. The strong correlation between population density and hate crime against gay men implies that Census data could be used to forecast the occurrence of hate crime in areas where no police records exist.