5 papers
Cure models: from mixture to matrix distributions
Martin Bladt, Jorge Yslas
Cure rate models address survival data in which a proportion of individuals will never experience the event of interest. Existing parametric approaches are predominantly based on f…
Assessing continuous common-shock risk through matrix distributions
Martin Bladt, Oscar Peralta, Jorge Yslas
We introduce a class of continuous-time bivariate phase-type distributions for modeling dependencies from common shocks. The construction uses continuous-time Markov processes that…
Bivariate phase-type distributions for experience rating in disability insurance
Christian Furrer, Jacob Juhl Sørensen, Jorge Yslas
In this paper, we consider the problem of experience rating within the classic Markov chain life insurance framework. We begin by establishing a link between mixed Poisson distribu…
Phase-type frailty models: A flexible approach to modeling unobserved heterogeneity in survival analysis
Jorge Yslas
Frailty models are essential tools in survival analysis for addressing unobserved heterogeneity and random effects in the data. These models incorporate a random effect, the frailt…
matrixdist: An R Package for Statistical Analysis of Matrix Distributions
Martin Bladt, Alaric Mueller, Jorge Yslas
The matrixdist R package provides a comprehensive suite of tools for the statistical analysis of matrix distributions, including phase-type, inhomogeneous phase-type, discrete phas…