Adjusted regularization in latent graphical models: application to multiple-neuron spike count data

DOI10.1214/18-AOAS1190zbMath1405.62210OpenAlexW2884403716WikidataQ91476825 ScholiaQ91476825MaRDI QIDQ1624826

Valérie Ventura, Giuseppe Vinci, Matthew A. Smith, Robert E. Kass

Publication date: 16 November 2018

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

Full work available at URL: https://projecteuclid.org/euclid.aoas/1532743486

zbMATH Keywords

high dimensionalityBayesian inferencesparsitylatent variable modelsGaussian graphical modelsLassoGaussian scale mixtureMacaque prefrontal cortexMacaque visual cortexPoisson-lognormalspike-counts

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (2)

Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM ⋮ Latent Network Structure Learning From High-Dimensional Multivariate Point Processes

Uses Software

• glasso
• BayesLogit

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