A nonparametric spatial test to identify factors that shape a microbiome

DOI10.1214/19-AOAS1262zbMath1435.62400arXiv1806.06297OpenAlexW2990552246MaRDI QIDQ2291523

Susheela P. Singh, Robert R. Dunn, Noah Fierer, Brian J. Reich, Ana-Maria Staicu

Publication date: 31 January 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.06297

zbMATH Keywords

Bayesian nonparametricsDirichlet processvariable selectionhigh dimensional dataspatial modelingspike-and-slab prior

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

A nonparametric spatial test to identify factors that shape a microbiome ⋮ A statistical perspective on the challenges in molecular microbial biology

Uses Software

• ElemStatLearn
• MIMIX

Cites Work

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• Sparse logistic principal components analysis for binary data
• A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• An introduction to copulas. Properties and applications
• Interpolation of spatial data. Some theory for kriging
• Needles and straw in a haystack: posterior concentration for possibly sparse sequences
• Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
• A nonparametric spatial test to identify factors that shape a microbiome
• A Bayesian analysis of some nonparametric problems
• Hybrid Dirichlet Mixture Models for Functional Data
• Regularized Sandwich Estimators for Analysis of High-Dimensional Data Using Generalized Estimating Equations
• Modelling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes
• Bayesian Variable Selection in Linear Regression
• The Spike-and-Slab LASSO
• MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments
• Spatial Joint Species Distribution Modeling
• Latent Stick-Breaking Processes
• Bayesian Factorizations of Big Sparse Tensors
• Multilevel Cross‐Dependent Binary Longitudinal Data
• Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing
• A Dirichlet-Tree Multinomial Regression Model for Associating Dietary Nutrients with Gut Microorganisms

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