Model diagnostics for multi-state models

Model diagnostics for smoothing spline ANOVA models

The author proposes some simple diagnostics for the assessment of the necessity of selected model terms in smoothing spline ANOVA models; the elimination of practically insignificant terms generally enhances the interpretability of the estimates, and sometimes may also have inferential implications. The diagnostics are derived from Kullback-Leibler geometry, and are illustrated in the settings ...

Multi-state models for event history analysis.

An introduction to event history analysis via multi-state models in given. Examples include the two-state model for survival analysis, the competing risks and illness-death models, and models for bone marrow transplantation. Statistical model specification via transition intensities and likelihood inference is introduced. Consequences of observational patterns are discussed, and a real example ...

Model Diagnostics for Proportional and Partial Proportional Odds Models

Bayesian semiparametric multi-state models

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 such parametric approaches to semiparametric models with flexible transition intensities based on Bay...

Bayesian Optimum Design Criterion for Multi Models Discrimination

The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an extension of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where...