Ancillaries and Conditional Inference

Ancillaries and Conditional Inference

Sufficiency has long been regarded as the primary reduction procedure to simplify a statistical model, and the assessment of the procedure involves an implicit global repeated sampling principle. By contrast, conditional procedures are almost as old and yet appear only occasionally in the central statistical literature. Recent likelihood theory examines the form of a general large sample statis...

Discussion of “ancillaries and Conditional Inference” by D.a.s. Fraser

1. THE EVOLUTION OF STATISTICAL THEORY Reliance on probability theory in statistical writing spans the spectrum from none, to fixed effects models, to random effects models, to Bayesian reasoning. One factor is the extent to which an author regards probability as a feature of the natural world. For a Bayesian, probability measures the strength of opinions, which are modeled by a sigma algebra. ...

Conditional Modeling and Conditional Inference

Conditional Probability and Defeasible Inference

We offer a probabilistic model of rational consequence relations (Lehmann and Magidor, 1990) by appealing to the extension of the classical Ramsey-Adams test proposed by Vann McGee in (McGee, 1994). Previous and influential models of non-monotonic consequence relations have been produced in terms of the dynamics of expectations (Gärdenfors and Makinson, 1994), (Gärdenfors, 1993). ‘Expectation’ ...

ctree: Conditional Inference Trees

This vignette describes the new reimplementation of conditional inference trees (CTree) in the R package partykit. CTree is a non-parametric class of regression trees embedding tree-structured regression models into a well defined theory of conditional inference procedures. It is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariat...