Parametric models are only occasionally used in the analysis of clinical studies of survival although they may offer advantages over Cox's model. In this paper, we report experiences that we have made fitting parametric models to data sets from different clinical trials mainly performed at the Vienna University Medical School. We emphasize the role of residuals for discriminating among candidat...

Recurrent event analysis has been a topic that has generated much attention in the last few years. The analysis of recurrent event data can be a complex task, with many different relationships and dependencies that need to be specified. One aspect of recurrent events that is difficult to specify, and even more complex to justify, is the relationship a subject’s rate of failure will have as even...

Summary Cox models with time-varying coefficients offer great flexibility in capturing the temporal dynamics of covariate effects on right-censored failure times. Because not all covariate coefficients are time varying, model selection for such models presents an additional challenge, which is to distinguish covariates with time-varying coefficient from those with time-independent coefficient. ...

We propose flexible models for multivariate realized volatility dynamics which involve generalizations of the Box-Cox transform to the matrix case. The matrix Box-Cox model of realized covariances (MBC-RCov) is based on transformations of the covariance matrix eigenvalues, while for the Box-Cox dynamic correlation (BC-DC) specification the variances are transformed individually and modeled join...

In this paper we study the geometry of 14 families K3 surfaces Picard number four with finite automorphism group, whose Neron—Severi lattices have been classified by È. B. Vinberg. We provide projective models, identify degrees a generating set Cox ring and in some cases prove unirationality associated moduli space.

For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions inverse Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates under-coverage confidence intervals. We propose modified debiased lasso method, which solves series quad...

We derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of efficient estimation in case-cohort designs, a type of two-phase design often used in cohort studies. The derivations make use of key lemmas appearing in Robins, Rotnitzky and Zhao [J. Amer. Statist. Assoc. 89 (1994) 846–8...