In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. Besides the inclusion of covariates, spatial effects are incorporated and modelled using a proper Gaussian conditional autoregressive prior based on Pettitt et al. (2002). Apart from...

Abstract In this paper the problem is investigated how to prove that after a specified time t 0 of operation the conditional survival probability of an item is less than the corresponding survival probability of a new item. This problem occurs in various areas like for instance in industry, when...

Maximum likelihood estimation and goodness-of-fit techniques are used within a competing risks framework to obtain maximum likelihood estimates of hazard, density, and survivor functions for randomly right-censored variables. Goodness-of- fit techniques are used to fit distributions to the crude...

This paper estimates recent default risk premia for U.S. corporate debt, based on a close relationship between default probabilities, as estimated by the Moody’s KMV EDF measure, and market default swap (CDS) rates. The default-swap data, obtained by CIBC from a large number of dealers...

In van der Laan and Dudoit (2003) we propose and theoretically study a unified loss function based statistical methodology, which provides a road map for estimation and performance assessment. Given a parameter of interest which can be described as the minimizer of the population mean of a loss...

Outcome prediction based on historical data has been of practical and theoretical interest in many disciplines. A common type of outcome prediction is binary or discrete outcome prediction, as found in medical diagnosis and firm bankruptcy prediction. The prediction problem studied in this paper...