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Conditional variable importance for random forests
TLDR
We identify two mechanisms responsible for this finding: (i) a preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme. Expand
Generating survival times to simulate Cox proportional hazards models.
TLDR
A general formula describing the relation between the hazard and the corresponding survival time of the Cox model is derived, which is useful in simulation studies. Expand
Introduction to imprecise probabilities
Preface Introduction Acknowledgements Outline of this Book and Guide to Readers Contributors 1 Desirability 1.1 Introduction 1.2 Reasoning about and with Sets of Desirable Gambles 1.2.1 RationalityExpand
Foundations of Probability
TLDR
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 uncertainty. Expand
Imprecision and Prior-data Conflict in Generalized Bayesian Inference
A great advantage of imprecise probability models over models based on precise, traditional probabilities is the potential to reflect the amount of knowledge they stand for. Consequently, impreciseExpand
Bayesian linear regression
TLDR
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 eliminate the influence of the prior). Expand
Unbiased split selection for classification trees based on the Gini Index
TLDR
A new split selection criterion that avoids variable selection bias in recursive partitioning algorithms based on standard impurity reduction. Expand
Nonparametric predictive inference and interval probability
The assumption A(n), proposed by Hill (J. Amer. Statist. Assoc. 63 (1968) 677), provides a natural basis for low structure non-parametric predictive inference, and has been justified in the BayesianExpand
Powerful algorithms for decision making under partial prior information and general ambiguity attitudes
TLDR
This paper discusses decision making in the practically important situation where only partial information on the stochastic behavior of the states of nature expressed by imprecise probabilities (interval probability) is available. Expand
Generalized basic probability assignments
  • Thomas Augustin
  • Mathematics, Computer Science
  • Int. J. Gen. Syst.
  • 1 August 2005
TLDR
Dempster–Shafer theory allows to construct belief functions from (precise) basic probability assignments, which allows for a very flexible modelling of uncertain knowledge. Expand
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