A default prior for regression coefficients

When the sample size is not too small, M-estimators of regression coefficients are approximately normal and unbiased. This leads to the familiar frequentist inference in terms of normality-based confidence intervals and p-values. From a Bayesian perspective, use of the (improper) uniform prior yields matching results in the sense that posterior quantiles agree with one-sided confidence bounds. For this, and various other reasons, the uniform prior is often considered objective or non-informative. In spite of this, we argue that the uniform prior is not suitable as a default prior for inference about a regression coefficient in the context of the bio-medical and social sciences. We propose that a more suitable default choice is the normal distribution with mean zero and standard deviation equal to the standard error of the M-estimator. We base this recommendation on two arguments. First, we show that this prior is non-informative for inference about the sign of the regression coefficient. Secondly, we show that this prior agrees well with a meta-analysis of 50 articles from the MEDLINE database

On the minimal travel time needed to collect n items on a circle

Consider n items located randomly on a circle of length 1. The locations of the items are assumed to be independent and uniformly distributed on [0,1). A picker starts at point 0 and has to collect all n items by moving along the circle at unit speed in either direction. In this paper we study the minimal travel time of the picker. We obtain upper bounds and analyze the exact travel time distribution. Further, we derive closed-form limiting results when n tends to infinity. We determine the behavior of the limiting distribution in a positive neighborhood of zero. The limiting random variable is closely related to exponential functionals associated with a Poisson process. These functionals occur in many areas and have been intensively studied in recent literature

Remembering Wassily Hoeffding

Wasssily Hoeffding's terminal illness and untimely death in 1991 put an end to efforts that were made to interview him for Statistical Science. An account of his scientific work is given in Fisher and Sen [The Collected Works of Wassily Hoeffding (1994) Springer], but the present authors felt that the statistical community should also be told about the life of this remarkable man. He contributed much to statistical science, but will also live on in the memory of those who knew him as a kind and modest teacher and friend, whose courage and learning were matched by a wonderful sense of humor.Comment: Published in at http://dx.doi.org/10.1214/08-STS271 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Multilingual Brain: An Experimental Study

This study investigates the organization of the mental lexicon in the multilingual brain in order to determine whether there is interaction between the lexicons of a speaker’s different languages. We performed a cross-language semantic priming experiment with Dutch native speakers having varying levels of French. We analysed our data in view of three models: the independent model (Kolers, 1963), the revised hierarchical model (Kroll & Steward, 2002) and the BIA+ model (Dijkstra et al, 2002). Our results support the independent model in which there is suggested that there is no interaction between the mental lexicons of the speakers’ languages

Versatility and ambiguity : first experiences with integrated territorial investment tool

The following article examines some of the initial experiences of Member States with the integrated territorial investment (ITI) tool. It draws from examples of ITI in Belgium (Limburg) and Finland (Six Cities) which are already in advanced stages of planning. The article highlights the versatility of ITI as an implementation tool for integrated territorial approaches but also considers key challenges in relation to administrative and institutional capacity, territorial competitiveness, limited scale of resource allocations, and ambiguities in relation to implementation requirements