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ABSTRACT. In this paper we consider statistical distributions of different types of patients on the patient lists of doctors. In our framework different types of patients have different preferences regarding their preferred choice of doctor. Assuming that the system is benefit efficient in the sense that distributions with larger total utility have higher probability, we can construct unique probability measures describing the statistical distribution of the different types of patients.

SUMMARYThe normal inverse Gaussian (NIG) distribution is a promising alternative for modelling financial data since it is a continuous distribution that allows for skewness and fat tails. There is an increasing number of applications of the NIG distribution to financial problems. Due to the complicated nature of its density, estimation procedures are not simple. In this paper we propose Bayesian estimation for the parameters of the NIG distribution via an MCMC scheme based on the Gibbs sampler. Our approach makes use of the data augmentation provided by the mixture representation of the distribution. We also extend the model to allow for modelling heteroscedastic regression situations. Examples with financial and simulated data are provided.

Mortality patterns and excess mortality have been studied and quantified in 103 patients treated with internal fixation for acute, displaced femoral neck fractures with special emphasis on the potential excess mortality which may follow later operations for capital necrosis, failure of the osteosynthesis, etc. Of 103 patients studied 31 needed one or more reoperations. We have confirmed previously published reports that excess mortality is limited to the first six months after the primary operation. Quantification of the excess mortality which may follow reoperations shows that later operations are not followed by an increased death rate compared with the standard population.

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