Multivariate matrix-exponential distributions

We review what is currently known about one-dimensional distributions on the non-negative reals with rational Laplace transform, also known as matrix-exponential distributions. In particular we discuss a flow interpreation which enables one to mimic certain probabilisticly inspired arguments which are known from the theory of phase-type distributions. We then move on to present ongoing research for higher dimensions. We discuss a characterization result, some closure properties, and a number of examples. Finally we present open problems and future perspectives

Distribution Fitting for Very Large Railway Delay Data Sets with Discrete Values

Modern railway signal systems allow the collection of very large data sets (more than a thousand values). These data sets are often rounded by the signal technology, so that the values are effectively discrete. This paper reviews other literature on fitting distributions to large data sets, and then shares the experience of distribution fitting to a large data set from the Danish railways

Distribution Fitting for Very Large Railway Delay Data Sets with Discrete Values

Modern railway signal systems allow the collection of very large data sets (more than a thousand values). These data sets are often rounded by the signal technology, so that the values are effectively discrete. This paper reviews other literature on fitting distributions to large data sets, and then shares the experience of distribution fitting to a large data set from the Danish railways.Modern railway signal systems allow the collection of very large data sets (more than a thousand values). These data sets are often rounded by the signal technology, so that the values are effectively discrete. This paper reviews other literature on fitting distributions to large data sets, and then shares the experience of distribution fitting to a large data set from the Danish railways