Book reviews

Distributed Cognitions: Psychological and Educational Considerations. Gavriel Salomon (Ed.), Cambridge & New York: Cambridge University Press, 1993. 275 pages, $49.95.

The Computer as Medium. Peter Bøgh Andersen, Berit Holmqvist, & Jens F. Jensen (Eds.), Cambridge: Cambridge University Press. 495 pages. $54.95.     

Regulatory fit effects on perceived fiscal exchange and tax compliance

Paying taxes can be considered a contribution to the welfare of a society. But even though tax payments are redistributed to citizens in the form of public goods and services, taxpayers often do not perceive many benefits from paying taxes. Information campaigns about the use of taxes for financing public goods and services could increase taxpayers’ understanding of the importance of taxes, strengthen their perception of fiscal exchange and consequently also increase tax compliance. Two studies examined how fit between framing of information and taxpayers’ regulatory focus affects perceived fiscal exchange and tax compliance. Taxpayers should perceive the exchange between tax payments and provision of public goods and services as higher if information framing suits their regulatory focus. Study 1 supported this hypothesis for induced regulatory focus. Study 2 replicated the findings for chronic regulatory focus and further demonstrated that regulatory fit also affects tax compliance. The results provide further evidence for findings from previous studies concerning regulatory fit effects on tax attitudes and extend these findings to a context with low tax morale.     

Bayesian regularisation in structured additive regression: a unifying perspective on shrinkage, smoothing and predictor selection

This paper surveys various shrinkage, smoothing and selection priors from a unifying perspective and shows how to combine them for Bayesian regularisation in the general class of structured additive regression models. As a common feature, all regularisation priors are conditionally Gaussian, given further parameters regularising model complexity. Hyperpriors for these parameters encourage shrinkage, smoothness or selection. It is shown that these regularisation (log-) priors can be interpreted as Bayesian analogues of several well-known frequentist penalty terms. Inference can be carried out with unified and computationally efficient MCMC schemes, estimating regularised regression coefficients and basis function coefficients simultaneously with complexity parameters and measuring uncertainty via corresponding marginal posteriors. For variable and function selection we discuss several variants of spike and slab priors which can also be cast into the framework of conditionally Gaussian priors. The performance of the Bayesian regularisation approaches is demonstrated in a hazard regression model and a high-dimensional geoadditive regression model.     

Order extensions,budget correspondences,and rational choice

This article is concerned with extensions of a continuous ordering R on a set X to a subset P(X) of the power set of X. The underlying topology will be the Hausdorff metric topology. We will see that continuous extensions of R do not require that P(X) contain every nonempty finite subset of X. Therefore, the analysis can be applied to consumer theory and inverse choice functions. In analogy to these functions budget correspondences are established which relate alternatives x with certain subsets of X, according to the extended ordering.     

Modelling count data with overdispersion and spatial effects

In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We account for unobserved heterogeneity in the data in two ways. On the one hand, we consider more flexible models than a common Poisson model allowing for overdispersion in different ways. In particular, the negative binomial and the generalized Poisson (GP) distribution are addressed where overdispersion is modelled by an additional model parameter. Further, zero-inflated models in which overdispersion is assumed to be caused by an excessive number of zeros are discussed. On the other hand, extra spatial variability in the data is taken into account by adding correlated spatial random effects to the models. This approach allows for an underlying spatial dependency structure which is modelled using a conditional autoregressive prior based on Pettitt et al. in Stat Comput 12(4):353–367, (2002). In an application the presented models are used to analyse the number of invasive meningococcal disease cases in Germany in the year 2004. Models are compared according to the deviance information criterion (DIC) suggested by Spiegelhalter et al. in J R Stat Soc B64(4):583–640, (2002) and using proper scoring rules, see for example Gneiting and Raftery in Technical Report no. 463, University of Washington, (2004). We observe a rather high degree of overdispersion in the data which is captured best by the GP model when spatial effects are neglected. While the addition of spatial effects to the models allowing for overdispersion gives no or only little improvement, spatial Poisson models with spatially correlated or uncorrelated random effects are to be preferred over all other models according to the considered criteria.