Non-parametric regression on compositional covariates using Bayesian P-splines.
Francesca BrunoFedele GrecoMassimo VentrucciPublished in: Stat. Methods Appl. (2016)
Keyphrases
- regression model
- gaussian processes
- gaussian process
- sparse bayesian learning
- relevance vector machine
- hyperparameters
- semi parametric
- response variable
- regression coefficients
- model selection
- explanatory variables
- bayesian learning
- regression analysis
- locally weighted
- regression methods
- regression problems
- posterior distribution
- bayesian networks
- b spline
- canonical correlation analysis
- dirichlet process mixture
- partial least squares
- closed form
- bayesian methods
- linear model
- regression method
- support vector regression
- sample size
- logistic regression
- data driven
- linear regression
- bayesian decision
- maximum likelihood
- model averaging
- multiple regression
- bayesian inference
- data sets
- support vector
- em algorithm
- parameter estimation
- kernel density estimation
- bayesian estimation
- probability density function