Title Participants Abstract
"On monotonically proceeding structures and stepwise increasing transition matrices of Markov chains" "Marie-Anne Guerry, Philippe Carette" "In general, the transition matrix of a Markov chain is a stochastic matrix. For a system that is modeled by a Markov chain, the transition matrix must reflect the characteristics of that system. The present paper introduces a particular class of transition matrices in order to model Markov systems for which, as the length of the time interval becomes greater, a transition from one state to another is more likely. We call these transition matrices stepwise increasing. Moreover in some contexts it is less desirable that the stocks fluctuate over time. In those situations, one is interested in monotonically proceeding stock vectors. This paper examines monotonically proceeding stock vectors and stepwise increasing transition matrices. We present conditions on the transition matrix such that all stock vectors are monotonically proceeding. In particular, the set of monotonically proceeding vectors is characterized for the two-state and three-state cases."
"Wiskunde voor bedrijfskundigen 2" "Philippe Carette"
"Wiskunde voor bedrijfskundigen 1" "Philippe Carette"
"Likelihood comparison of alternative Markov models incorporating duration of stay" "Marie-Anne Guerry, Philippe Carette" "Markov chains are commonly used to model transitions in a system partitioned into categories. In manpower planning models these categories are, for example, job levels or grades in the firm under study. Building a Markov model starts with selecting its states that are assumed to be homogeneous; i.e. the system units in a same state have similar transition probabilities. For systems where the transitions among the categories depend on the duration of stay in the outgoing categories, previous work considered Markov models where the states are subdivisions of the categories into duration of stay intervals, and the more complex semi-Markov models. The present work investigates alternative Markov models for systems where the categories have transition probabilities depending on the duration of stay by selecting the states in different ways: state selection by duration intervals and state selection by duration values. The resulting Markov models are compared based on the likelihood of a set of panel data given the model. For a system with two categories, we prove that the model with states defined by duration values has a better maximum likelihood fit than the base model having the initial categories as states, while this is not the case for the model with states defined by duration intervals under conditions that seem realistic in practice. Although the duration-interval approach is considered in previous studies, the likelihood-comparison is less in favor of this model."
"Competing Markov manpower models and states based on seniority" "Philippe Carette, Marie-Anne Guerry"
"Leader-employee congruence of expected contributions in the employee-organization relationship" "Mieke Audenaert, Philippe Carette, Lynn M. Shore, Thomas Lange, Thomas Van Waeyenberg, Adelien Decramer"
"Modelling grade seniority in manpower planning: Markov or Semi-Markov?" "Philippe Carette, Marie-Anne Guerry"
"Model Selection Within the Class of Discrete-Time Markovian Models" "Marie-Anne Guerry, Philippe Carette"
"On stepwise increasing roots of transition matrices" "Philippe Carette, Marie-Anne Guerry"
"Coproductie van veiligheid in buurtinformatienetwerken : een analyse van gepercipieerde meerwaarde" "Bram Verschuere, Philippe Carette"