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作者：Buehlmann, P.; Kalisch, M.; Maathuis, M. H.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We consider variable selection in high-dimensional linear models where the number of covariates greatly exceeds the sample size. We introduce the new concept of partial faithfulness and use it to infer associations between the covariates and the response. Under partial faithfulness, we develop a simplified version of the PC algorithm (Spirtes et al., 2000), which is computationally feasible even with thousands of covariates and provides consistent variable selection under conditions on the ran...
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作者：Gandy, A.; Kvaloy, J. T.; Bottle, A.; Zhou, F.
作者单位：Imperial College London; Universitetet i Stavanger; Imperial College London; Imperial College London
摘要：Recently there has been interest in risk-adjusted cumulative sum charts, CUSUMs, to monitor the performance of e. g. hospitals, taking into account the heterogeneity of patients. Even though many outcomes involve time, only conventional regression models are commonly used. In this article we investigate how time to event models may be used for monitoring purposes. We consider monitoring using CUSUMs based on the partial likelihood ratio between an out-of-control state and an in-control state. ...
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作者：Lakhal-Chaieb, M. L.
作者单位：Laval University
摘要：This paper discusses copula model selection procedures and goodness-of-fit tests under censoring. The proposed methodology is based on a comparison of nonparametric and model-based estimators of the probability integral transformation, K. New weighted estimators for K are introduced. The resulting tests are compared to an existing approach by simulation and illustrated with an example involving bleeding changes in a woman's reproductive history.
