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Conditional variable importance for random forests
A new, conditional permutation scheme is developed for the computation of the variable importance measure that reflects the true impact of each predictor variable more reliably than the original marginal approach.
Generating survival times to simulate Cox proportional hazards models
Simulation studies present an important statistical tool to investigate the performance, properties and adequacy of statistical models in pre‐specified situations. One of the most important
Introduction to imprecise probabilities
This book discusses the construction of Sets of Desirable Gambles, a model for evaluating the relationship between self-consistency and freedom, and its applications in the context of self-confidence and self-regulation.
Foundations of Probability
Probability theory is that part of mathematics that is concerned with the description and modeling of random phenomena, or in a more general — but not unanimously accepted — sense, of any kind of
Bayesian linear regression
The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to
Imprecision and Prior-data Conflict in Generalized Bayesian Inference
This paper considers a general class of recently studied imprecise probability models, including the Imprecise Dirichlet Model under prior information, and more generally the framework of Quaeghebeur and de Cooman for imprecising inference in canonical exponential families, and proposes an extension reestablishing the natural relationship between knowledge and imprecision.
Powerful algorithms for decision making under partial prior information and general ambiguity attitudes
The paper develops powerful algorithms for determining optimal actions under arbitrary ambiguity attitudes and the criterion of E-admissibility based on linear programming and can be implemented by standard software.
An Exact Corrected Log‐Likelihood Function for Cox's Proportional Hazards Model under Measurement Error and Some Extensions
Abstract.  This paper studies Cox's proportional hazards model under covariate measurement error. Nakamura's [Biometrika 77 (1990) 127] methodology of corrected log‐likelihood will be applied to the