Conference Agenda

The pseudo-observation regression approach provides a flexible alternative to the omnipresent proportional hazards model when modeling time-to-event outcomes. In this approach, estimands representable as expectations are fitted to regression models using covariates of interest. Exemplary estimands that fit this framework are the restricted mean time lost (in competing risks models) or the survival function at a fixed time-point (in simple survival models).
Even though consistent parameter estimates are readily obtained using standard statistical software, variance estimation turns out to be a more intricate task: We verify the longstanding conjecture that the usual Huber-White estimator is not consistent. By confirming that a plug-in estimator can be used instead, we obtain asymptotically exact and consistent tests for general linear hypotheses in the parameters of the model. Additionally, we confirm that naive bootstrapping can not be used for covariance estimation in the pseudo-observation approach either. However, it can still be used for hypothesis testing by applying a suitable studentization. These methods are evaluated in an extensive simulation study and exemplified with a real data analysis.

Dirichlet mixture models (DMMs) provide a flexible and interpretable framework for clustering and modeling compositional data and have found widespread application in genomics, ecology, and the social sciences. Despite their popularity, formal likelihood-based inference for DMM parameters remains underdeveloped, primarily due to the presence of simplex constraints on mixture weights and the complex dependence structure induced by latent component memberships. In this paper, we develop a unified framework for classical likelihood-based inference in Dirichlet mixture models by working on an unconstrained parameterization that combines an additive log-ratio transformation of the mixture weights with the original Dirichlet concentration parameters. Within this framework, we derive closed-form expressions for score functions and observed Fisher information matrices, including full cross-component information terms obtained via the Louis identity. These results enable the construction of Wald, score (Lagrange Multiplier), and likelihood ratio tests for a broad class of regular parametric hypotheses, including fixed-value restrictions and equality constraints across mixture components. We show how the proposed methods apply seamlessly to both soft and hard EM-based estimation schemes and provide a numerically stable implementation that yields consistent standard errors and confidence intervals on the original parameter scale. Through simulation experiments and a real-data application, we demonstrate that the proposed inferential procedures perform well in finite samples and provide meaningful uncertainty quantification for DMM parameters.