In this article we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variable are modelles through a flexible semiparametric predictor. We extend existing LVM with simple linear covariate effects by including...

Multi-state models provide a unified framework for the description of the evolution of discrete phenomena in continuous time. One particular example are Markov processes which can be characterised by a set of time-constant transition intensities between the states. In this paper, we will extend...

Extensions of the traditional Cox proportional hazard model, concerning the following features are often desirable in applications: Simultaneous nonparametric estimation of baseline hazard and usual fixed covariate effects, modelling and detection of time-varying covariate effects and nonlinear...

Nonparametric Predictive Inference (NPI) is a general methodology to learn from data in the absense of prior knowledge and without adding unjustified assumptions. This paper develops NPI for multinominal data where the total number of possible categories for the data is known. We present the...