Random effects and shrinkage estimation in capture-recapture models

We discuss the analysis of random effects in capture-recapture models, and outline Bayesian and frequentists approaches to their analysis. Under a normal model, random effects estimators derived from Bayesian or frequentist considerations have a common form as shrinkage estimators. We discuss some of the difficulties of analysing random effects using traditional methods, and argue that a Bayesian formulation provides a rigorous framework for dealing with these difficulties. In capture-recapture models, random effects may provide a parsimonious compromise between constant and completely time-dependent models for the parameters (e.g. survival probability). We consider application of random effects to band-recovery models, although the principles apply to more general situations, such as Cormack-Jolly-Seber models. We illustrate these ideas using a commonly analysed band recovery data set.     

Model selection using information criteria under a new estimation method: least squares ratio

In this study, we evaluate several forms of both Akaike-type and Information Complexity (ICOMP)-type information criteria, in the context of selecting an optimal subset least squares ratio (LSR) regression model. Our simulation studies are designed to mimic many characteristics present in real data – heavy tails, multicollinearity, redundant variables, and completely unnecessary variables. Our findings are that LSR in conjunction with one of the ICOMP criteria is very good at selecting the true model. Finally, we apply these methods to the familiar body fat data set.     

The changing structure of food retailing in europe: the implications for strategy

European food retailers have increased their scale and scope while concentrating sales into large stores. Strategic implications include the need for scale to compete and organisational relationships to move from supply push to demand pull retailing. Consequences are different for private label and for branded manufacturers, who must respond to the demands of retail supply chains whilst retaining control of product development. Further concentration in both sectors is likely. Consumers are more mobile, but more captive to the retailer in store. Public policy makers are challenged to find new measures of retail power and both allow for innovation and ensure equitable shopping opportunities.     

An alternate version of the conceptual predictive statistic based on a symmetrized discrepancy measure

The conceptual predictive statistic, C_p, is a widely used criterion for model selection in linear regression. C_p serves as an estimator of a discrepancy, a measure that reflects the disparity between the generating model and a fitted candidate model. This discrepancy, based on scaled squared error loss, is asymmetric: an alternate measure is obtained by reversing the roles of the two models in the definition of the measure. We propose a variant of the C_p statistic based on estimating a symmetrized version of the discrepancy targeted by C_p. We claim that the resulting criterion provides better protection against overfitting than C_p, since the symmetric discrepancy is more sensitive towards detecting overspecification than its asymmetric counterpart. We illustrate our claim by presenting simulation results. Finally, we demonstrate the practical utility of the new criterion by discussing a modeling application based on data collected in a cardiac rehabilitation program at University of Iowa Hospitals and Clinics.     

Approximation of power in multivariate analysis

We consider the calculation of power functions in classical multivariate analysis. In this context, power can be expressed in terms of tail probabilities of certain noncentral distributions. The necessary noncentral distribution theory was developed between the 1940s and 1970s by a number of authors. However, tractable methods for calculating the relevant probabilities have been lacking. In this paper we present simple yet extremely accurate saddlepoint approximations to power functions associated with the following classical test statistics: the likelihood ratio statistic for testing the general linear hypothesis in MANOVA; the likelihood ratio statistic for testing block independence; and Bartlett's modified likelihood ratio statistic for testing equality of covariance matrices.     

Exploring Neglected Dimensions of Social Policy: The SDW, Fiscal Welfare and the Exemplar of Local Taxation in England

Titmuss's Social Division of Welfare (SDW) thesis is a vitally important but much neglected element of social policy analysis. This article seeks to explore the SDW, with a particular focus on fiscal welfare. Fiscal welfare has been described as forming a hidden welfare state, and while taxation is one of the main ways in which governments affect the lives of citizens, studies of welfare pay remarkably little attention to its impact. Fiscal welfare is examined by using, as an exemplar, local taxation in England, a subject that itself is neglected within social policy. Local taxation in England is of interest because it illustrates the impact of a system of taxation on different groups of citizens, and how this can operate to the benefit of rich over poor citizens. This is because the current system is highly regressive, meaning that those on low and middle incomes spend proportionately more of their income paying the tax than do those on high incomes. What is of further interest is how within the debate about reform of local taxation, concern with regressivity becomes obscured and ceases to be the focus of attention. We are thus provided with an example of how fiscal welfare remains a hidden issue. The article concludes by arguing that social policy analysis needs to move beyond the narrow confines of social welfare and develop a broader understanding of welfare, based on the SDW.