Results 1 to 10 of about 66,452 (307)
A Lego System for Conditional Inference [PDF]
The American Statistician, 2006 Conditioning on the observed data is an important and flexible design principle for statistical test procedures. Although generally applicable, permutation tests currently in use are limited to the treatment of special cases, such as contingency tables or K-sample problems. A new theoretical framework for permutation tests opens up the way to a unified
Hothorn, Torsten +3 more
openaire +6 more sources
Randomization Tests that Condition on Non-Categorical Covariate Balance
Journal of Causal Inference, 2019 A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment effects.
Branson Zach, Miratrix Luke W.
doaj +1 more source
Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning
Entropy, 2021 It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction.
Chi-Ken Lu, Patrick Shafto
doaj +1 more source
Conditional Inference with a Functional Nuisance Parameter [PDF]
SSRN Electronic Journal, 2014 This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite-dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter and propose conditional tests.
Andrews, Isaiah, Mikusheva, Anna
openaire +5 more sources
Model Free Inference on Multivariate Time Series with Conditional Correlations
Stats, 2020 New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in
Dimitrios Thomakos +2 more
doaj +1 more source
BMC Medical Research Methodology, 2022 Background Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model ...
Michael Kammer +3 more
doaj +1 more source
Conditionals As Representative Inferences [PDF]
Axiomathes, 2020 AbstractAccording to Adams (Inquiry 8:166–197, 1965), the acceptability of an indicative conditional goes with the conditional probability of the consequent given the antecedent. However, some conditionals seem to be inappropriate, although their corresponding conditional probability is high.
Robert van Rooij, Katrin Schulz
openaire +3 more sources
Review about the Permutation Approach in Hypothesis Testing
MathematicsToday, permutation tests represent a powerful and increasingly widespread tool of statistical inference for hypothesis-testing problems. To the best of our knowledge, a review of the application of permutation tests for complex data in practical data ...
Stefano Bonnini +2 more
doaj +1 more source
Brain Imaging, Forward Inference, and Theories of Reasoning
Frontiers in Human Neuroscience, 2015 This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006).
Evan eHeit
doaj +1 more source
Bayesian reasoning with ifs and ands and ors
Frontiers in Psychology, 2015 The Bayesian approach to the psychology of reasoning generalizes binary logic, extending the binary concept of consistency to that of coherence, and allowing the study of deductive reasoning from uncertain premises.
Nicole eCruz +3 more
doaj +1 more source