Bayesian model comparison and model averaging for small-area estimation

This paper considers small-area estimation with lung cancer mortality data, and discusses the choice of upper-level model for the variation over areas. Inference about the random effects for the areas may depend strongly on the choice of this model, but this choice is not a straightforward matter. We give a general methodology for both evaluating the data evidence for different models and averaging over plausible models to give robust area effect distributions. We reanalyze the data of Tsutakawa [Biometrics 41 (1985) 69--79] on lung cancer mortality rates in Missouri cities, and show the differences in conclusions about the city rates from this methodology.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS205 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Fast Generation of Discrete Random Variables

We describe two methods and provide C programs for generating discrete random variables with functions that are simple and fast, averaging ten times as fast as published methods and more than five times as fast as the fastest of those. We provide general procedures for implementing the two methods, as well as specific procedures for three of the most important discrete distributions: Poisson, binomial and hypergeometric.

On the competition of asymetric agents

Rank-order tournaments are usually implemented in organizations to provide incentives for eliciting employees’ effort and/or to identify the agent with the higher ability, e.g. in promotion tournaments. We close a gap in the literature by experimentally analyzing a ceteris paribus variation of the prize spread – being the major design feature of tournaments – in a symmetric and an asymmetric setting. We find that effort significantly increases with the prize spread as predicted by standard theory. However, only if the prize spread is sufficiently large weak players competing against strong players strain themselves all the more and sorting of agents is feasible

Incentive Effects in Asymmetric Tournaments Empirical Evidence from the German Hockey League

Following tournament theory, incentives will be rather low if the contestants of a tournament are heterogeneous. We empirically test this prediction using a large dataset from the German Hockey League. Our results show that indeed the intensity of a game is lower if the teams are more heterogeneous. This effect can be observed for the game as a whole as well as for the ?rst and last third. When dividing the teams in the dataset into favorites and underdogs, we only observe a reduction of effort provision from favorite teams. As the number of games per team changes between different seasons, we can also investigate the effect of a changing spread between winner and loser prize. In line with theory, teams reduce effort if the spread declines. Interestingly, effort is also sensitive to the total number of teams in the league even if the price spread remains unchanged