Knowing the gap - intermediate information in tournaments

Intermediate information is often available to competitors in dynamic tournaments. We develop two simple tournament models with two stages: one with intermediate information on subjects’ relative positions after the first stage, one without. In our models, equilibrium behavior in both stages is not changed by intermediate information. We test our formal analysis using data from laboratory experiments. We find no difference between average first and second stage efforts. With intermediate information, however, subjects adjust their effort to a higher extent. Subjects who lead tend to lower their second stage effort, subjects who lag still try to win the tournament. Overall, intermediate information does not endanger the effectiveness of rank-order tournaments: incentives do neither break down nor does a rat race arise. We also briefly investigate costly intermediate information

Nucleus Driven Electronic Pulsation

We derive and solve by the spectral method the equations for a neutral system of ultra-relativistic electrons that are compressed to the radius of the nucleus and subject to a driving force. This driving force can be thought of as originating from a nuclear breathing mode, a possibility we discuss in detail

Enterprise application reuse: Semantic discovery of business grid services

Web services have emerged as a prominent paradigm for the development of distributed software systems as they provide the potential for software to be modularized in a way that functionality can be described, discovered and deployed in a platform independent manner over a network (e.g., intranets, extranets and the Internet). This paper examines an extension of this paradigm to encompass ‘Grid Services’, which enables software capabilities to be recast with an operational focus and support a heterogeneous mix of business software and data, termed a Business Grid - "the grid of semantic services". The current industrial representation of services is predominantly syntactic however, lacking the fundamental semantic underpinnings required to fulfill the goals of any semantically-oriented Grid. Consequently, the use of semantic technology in support of business software heterogeneity is investigated as a likely tool to support a diverse and distributed software inventory and user. Service discovery architecture is therefore developed that is (a) distributed in form, (2) supports distributed service knowledge and (3) automatically extends service knowledge (as greater descriptive precision is inferred from the operating application system). This discovery engine is used to execute several real-word scenarios in order to develop and test a framework for engineering such grid service knowledge. The examples presented comprise software components taken from a group of Investment Banking systems. Resulting from the research is a framework for engineering servic

Dynamic Modelling of Child Mortality in Developing Countries: Application for Zambia

In this paper, we analyse the causes of under five mortality in Zambia, with a particular emphasis on assessing possible time-variations in the effects of covariates, i.e. whether the effects of certain covariates vary with the age of the child. The analysis is based on micro data from the 1992 Demographic and health Survey. Employing a Bayesian dynamic logit model for discrete time survival data and Markov-Chain Monte Carlo methods, we find that there are several variables, including the age of the mother and the breastfeeding duration whose effects exhibit distinct age-dependencies. In the case of breastfeeding, this age dependency is intimately linked with the reasons for stopping breastfeeding. Incorporating such age dependencies greatly improves the explanatory power of the model and yields new insights on the differential role of covariates on child survival

Semiparametric Bayesian Time-Space Analysis of Unemployment Duration

In this paper, we analyze unemployment duration in Germany with official data from the German Federal Employment Office for the years 1980-1995. Conventional hazard rate models for leaving unemployment cannot cope with simultaneous and flexible fitting of duration dependence, nonlinear covariate effects, trend and seasonal calendar time components and a large number of regional effects. We apply a semiparametric hierarchical Bayesian modelling approach that is suitable for time-space analysis of unemployment duration by simultaneously including and estimating effects of several time scales, regional variation and further covariates. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques

Penalized additive regression for space-time data: a Bayesian perspective

We propose extensions of penalized spline generalized additive models for analysing space-time regression data and study them from a Bayesian perspective. Non-linear effects of continuous covariates and time trends are modelled through Bayesian versions of penalized splines, while correlated spatial effects follow a Markov random field prior. This allows to treat all functions and effects within a unified general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference can be performed either with full (FB) or empirical Bayes (EB) posterior analysis. FB inference using MCMC techniques is a slight extension of own previous work. For EB inference, a computationally efficient solution is developed on the basis of a generalized linear mixed model representation. The second approach can be viewed as posterior mode estimation and is closely related to penalized likelihood estimation in a frequentist setting. Variance components, corresponding to smoothing parameters, are then estimated by using marginal likelihood. We carefully compare both inferential procedures in simulation studies and illustrate them through real data applications. The methodology is available in the open domain statistical package BayesX and as an S-plus/R function

Semiparametric Analysis of the Socio-Demographic and Spatial Determinants of Undernutrition in Two African Countries

We estimate semiparametric regression models of chronic undernutrition (stunting) using the 1992 Demographic and Health Surveys (DHS) from Tanzania and Zambia. We focus particularly on the influence of the child's age, the mother's body mass index, and spatial influences on chronic undernutrition. Conventional parametric regression models are not flexible enough to cope with possibly nonlinear effects of the continuous covariates and cannot flexibly model spatial influences. We present a Bayesian semiparametric analysis of the effects of these two covariates on chronic undernutrition. Moreover, we investigate spatial determinants of undernutrition in these two countries. Compared to previous work with a simple fixed effects approach for the influence of provinces, we model small scale district specific effects using flexible spatial priors. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques