Discrete Conditional Phase-type models (DC-Ph) consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling...

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed...

This paper presents a methodology to find near-optimal joint inventory control policies for the real case of a one-warehouse, n-retailer distribution system of infusion solutions at a University Medical Center in France. We consider stochastic demand, batching and order-up-to level policies as...

The increase in cost of supplies and services is outpacing the increase in revenues at many hospitals. To address this cost increase hospitals are seeking more efficient ways to store and manage vast inventories of medical supplies. A parsimonious and efficient inventory system which we call...

Purpose – This paper seeks to investigate the effect of the PricewaterhouseCoopers (PwC) merger on the market for audit services in the UK. To this end a “what if” analysis is conducted comparing estimated outcomes prior to the merger with those expected under post‐merger conditions....

Purpose – This paper aims to extend the Fama and French (FF) three‐factor model in studying time‐varying risk premiums of Sector Select Exchange Traded Funds (ETFs) under a Markov regime‐switching framework. Design/methodology/approach – First, the original FF model is augmented to...

Purpose – The purpose of this paper is to investigate whether Markov mixture of normals (MMN) model is a viable approach to modeling financial returns. Design/methodology/approach – This paper adopts the full Bayesian estimation approach based on the method of Gibbs sampling, and the latent...