In this paper, we present a modulated Poisson process model to describe and analyze arrival data to a call center. The attractive feature of this model is that it takes into account both covariate and time effects on the call volume intensity, and in so doing, enables us to assess the...

In this paper, we study the drivers of customer satisfaction for financial services. We discuss a full Bayesian analysis based on data collected from customers of a leading financial services company. Our approach allows us to explicitly accommodate missing data and enables quantitative...

The stock market displays regime switching between upturns and downturns. This paper provides a Bayesian framework for making portfolio decisions that takes this regime switching into account, together with asset pricing model uncertainty and parameter uncertainty. The findings reveal that the...

The majority of papers on stochastic inventory theory make the assumption that the distribution of consumer demand in each time period is known with certainty. While this assumption is unsupported in many applied contexts, it is conventionally held that more realistic models are more difficult...

Data on accident precursors can help in estimating accident frequencies, since they provide a rich source of information on intersystem dependencies. However, Bayesian analysis of accident precursors requires the ability to construct joint prior distributions reflecting such dependencies. For...

This paper introduces a Bayesian decision theoretic model of optimal production in the presence of learning-curve uncertainty. The well-known learning-curve model is extended to allow for random variation in the learning process with uncertainty regarding some parameter of the variation. A...

We consider the long-run odds that narcotics users remain abstinent after methadone treatment. A flexible split-hazard specification that allows for individual-level differences in both the long-run probability of eventual relapse and the short-run timing of relapse is developed. The model is...

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...

We analyze the duration and outcomes of patent examination at the European Patent Office utilizing an unusually rich data set covering a random sample of 215,265 applications filed between 1982 and 1998. In our empirical analysis, we distinguish between three groups of determinants: applicant...