ArticleJournal of multivariate analysis2012
Nonparametric Bayes Classification and Hypothesis Testing on Manifolds.
Article in Journal of multivariate analysis, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Latent Nested Nonparametric Priors (with Discussion).Bayesian analysis · 2019Article
- Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions.Journal of the American Statistical Association · 2019Article
- A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support.BMC bioinformatics · 2017Article
- Conditional local distance correlation for manifold-valued data.Information processing in medical imaging : proceedings of the ... conference · 2017Article
- Regression Models on Riemannian Symmetric Spaces.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2017Article
- Classification via Bayesian Nonparametric Learning of Affine Subspaces.Journal of the American Statistical Association · 2013Article
- Strong consistency of nonparametric Bayes density estimation on compact metric spaces with applications to specific manifolds.Annals of the Institute of Statistical Mathematics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.