1,722 research outputs found
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
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