James - stein regression estimation in a prediction context

Two types of shrunken estimators of regression coefficients are studied in the context of stochastic predictor variables. Emphasis is placed on the choice of the shrinkage parameters, using a ‘deleted-observation’ concept which is motivated by predictive ability.     

New Data,Strategies, and Insights for Listeria monocytogenes Dose‐Response Models: Summary of an Interagency Workshop, 2011

Listeria monocytogenes is a leading cause of hospitalization, fetal loss, and death due to foodborne illnesses in the United States. A quantitative assessment of the relative risk of listeriosis associated with the consumption of 23 selected categories of ready‐to‐eat foods, published by the U.S. Department of Health and Human Services and the U.S. Department of Agriculture in 2003, has been instrumental in identifying the food products and practices that pose the greatest listeriosis risk and has guided the evaluation of potential intervention strategies. Dose‐response models, which quantify the relationship between an exposure dose and the probability of adverse health outcomes, were essential components of the risk assessment. However, because of data gaps and limitations in the available data and modeling approaches, considerable uncertainty existed. Since publication of the risk assessment, new data have become available for modeling L. monocytogenes dose‐response. At the same time, recent advances in the understanding of L. monocytogenes pathophysiology and strain diversity have warranted a critical reevaluation of the published dose‐response models. To discuss strategies for modeling L. monocytogenes dose‐response, the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) held a scientific workshop in 2011 (details available at http://foodrisk.org/irac/events/ ). The main findings of the workshop and the most current and relevant data identified during the workshop are summarized and presented in the context of L. monocytogenes dose‐response. This article also discusses new insights on dose‐response modeling for L. monocytogenes and research opportunities to meet future needs.     

The distinct role of group-central and group-peripheral norms in taxpaying behaviour

In recent years, a social identity approach has been used to help understand why people do or do not pay tax [see Taylor, 2003; Wenzel, M., 2002. The impact of outcome orientation and justice concerns on tax compliance: the role of taxpayers’ identity. Journal of Applied Psychology 87, 629–645; Wenzel, M., 2004. An analysis of norm processes in tax compliance. Journal of Economic Psychology 25, 213–228; Wenzel, M., 2005. Misperception of social norms about tax compliance: from theory to intervention. Journal of Economic Psychology 26, 862–883; Wenzel, M., 2007. The multiplicity of taxpayer identities and their implications for tax ethics. Law & Policy 29, 31–50]. This research, which has focused almost exclusively on national identity, indicates that the more people identify with a group, the more likely they are to adhere to its tax norms and values. However, conformity to group norms may be more nuanced than this, and depend on (a) the meaning or content of the identity in question [e.g., Turner, J.C., 1999. Some current themes in research on social identity and self-categorization theories. In: Ellemers, N., Spears, R., Doojse, B. (Eds.), Social Identity: Context, Commitment, Content. Blackwell, Oxford, pp. 6–34] and (b) whether the norms and values are central or peripheral to the content of that identity. In line with this idea, two studies explored whether the concept and act of taxpaying are more central to what it means to be a member of one's nation than of one's occupational group. Both studies confirm this expectation. Importantly, the findings also suggest that although occupational groups have different norms and values in relation to pre-tax behaviours (e.g., how to deal with extra income), these too can be peripheral to what it means to a group member. If norms are peripheral to identity content, conformity to such norms may be independent of group identification.     

On the α-risks for shewhart control charts

The performance of the usual Shewhart control charts for monitoring process means and variation can be greatly affected by nonnormal data or subgroups that are correlated. Define the α_k-risk for a Shewhart chart to be the probability that at least one “out-of-control” subgroup occurs in k subgroups when the control limits are calculated from the k subgroups. Simulation results show that the α_k-risks can be quite large even for a process with normally distributed, independent subgroups. When the data are nonnormal, it is shown that the α_k-risk increases dramatically. A method is also developed for simulating an “in-control” process with correlated subgroups from an autoregressive model. Simulations with this model indicate marked changes in the α_k-risks for the Shewhart charts utilizing this type of correlated process data. Therefore, in practice a process should be investigated thoroughly regarding whether or not it is generating normal, independent data before out-of-control points on the control charts are interpreted to be due to some real assignable cause.     

The breakdown and influence properties of outlier rejection-plus-mean procedures

In a recent paper, Hampel (1985) studied the properties of rejection-plus-mean procedures as estimators of a location parameter. He reported that these procedures have low breakdown and high variance. In this article it is pointed out that these results are due to the outliers being rejected in a forwards-stepping manner, and when a more appropriate backwards-stepping approach is used, rejection-plus-mean procedures lead to estimators with high breakdown and high variance. In this article it is pointed out that these results are due to the outliers being rejected in a forwards-stepping manner, and when a more appropriate backwards-stepping approach is used, rejection-plus-mean procedures lead to estimator with high breakdown and redescending theoretical influence function.     

Bootstrap method in ranking and slippage problems

In this paper, the bootstrap method of Efron (1979) is given for a ranking and a slippage problem, where the ranking (or slippage) is with respect to the mean of the distributions. The method is also applied to obtain a confidence interval for the largest mean.     

Minimax property of stein's estimator

Stein's estimator and some other estimators of the mean of a K-variate normal distribution are known to dominate the maximum likelihood estimator under quadratic loss for K > 3, and are therefore minimax. In this paper it is shown that the minimax property of Stein's rule is preserved with respect to a generalized loss function.