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elrm: Software Implementing Exact-Like Inference for Logistic Regression Models [PDF]
Journal of Statistical Software, 2007 Exact inference is based on the conditional distribution of the sufficient statistics for the parameters of interest given the observed values for the remaining sufficient statistics.
David Zamar, Brad McNeney, Jinko Graham
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A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data [PDF]
BMC Medical Research Methodology, 2017 Background Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data.
Justine B. Nasejje +3 more
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Ancillaries and Conditional Inference
Statistical Science, 2004 Sufficiency has long been regarded as the primary reduction pro- cedure to simplify a statistical model, and the assessment of the procedure involves an implicit global repeated sampling principle. By contrast, condi- tional procedures are almost as old and yet appear only occasionally in the central statistical literature.
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Conditional Inference under Disjunctive Rationality [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The question of conditional inference, i.e., of which conditional sentences of the form ``if A then, normally, B'' should follow from a set KB of such sentences, has been one of the classic questions of AI, with several well-known solutions proposed. Perhaps the most notable is the rational closure construction of Lehmann and Magidor, under which the ...
Booth, Richard, Varzinczak, Ivan
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Position Fixing and Uncertainty [PDF]
TransNav, 2023 Taken random observations are usually accompanied by rectified knowledge regarding their behaviour. In modern computer applications, raw data sets are usually exploited at learning phase.
Wlodzimierz Filipowicz
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Conditional as-if analyses in randomized experiments
Journal of Causal Inference, 2021 The injunction to “analyze the way you randomize” is well known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the merits of randomization-based inference seldom follow this injunction to ...
Pashley Nicole E. +2 more
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Distribution-free conditional median inference [PDF]
Electronic Journal of Statistics, 2021 We consider the problem of constructing confidence intervals for the median of a response $Y \in \mathbb{R}$ conditional on features $X \in \mathbb{R}^d$ in a situation where we are not willing to make any assumption whatsoever on the underlying distribution of the data $(X,Y)$.
Medarametla, Dhruv, Candès, Emmanuel
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Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Conditional knowledge bases consisting of qualitative conditionals play a predominant role in knowledge representation and reasoning. In this paper, we develop a full map of all consistent conditional knowledge bases over a small signature in different ...
Christoph Beierle +2 more
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Conditionals, Infeasible Worlds, and Reasoning with System W
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 The recently introduced notion of an inductive inference operator captures the process of completing a given conditional belief base to an inference relation.
Jonas Haldimann +3 more
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How Should We Quantify Uncertainty in Statistical Inference?
Frontiers in Ecology and Evolution, 2020 An inferential statement is any statement about the parameters, form of the underlying process or future outcomes. An inferential statement, that provides an approximation to the truth, becomes “statistical” only when there is a measure of uncertainty ...
Subhash R. Lele
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