Abstract
Hierarchical models enable the encoding of a variety of parametric structures. However, when presented with a large number of covariates upon which some component of a model hierarchy depends, the modeller may be unwilling or unable to specify a form for that dependence. Data-mining methods are designed to automatically discover relationships between many covariates and a response surface, easily accommodating non-linearities and higher-order interactions. We present a method of wrapping hierarchical models around data-mining methods, preserving the best qualities of the two paradigms. We fit the resulting semi-parametric models using an approximate Gibbs sampler called HEBBRU. Using a simulated dataset, we show that HEBBRU is useful for exploratory analysis and displays excellent predictive accuracy. Finally, we apply HEBBRU to an ornithological dataset drawn from the eBird database.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1753-1766 |
| Number of pages | 14 |
| Journal | Journal of Statistical Computation and Simulation |
| Volume | 82 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2012 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics
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Embedding black-box regression techniques into hierarchical Bayesian models. / Shaby, Benjamin A.; Fink, Daniel.
In: Journal of Statistical Computation and Simulation, Vol. 82, No. 12, 01.12.2012, p. 1753-1766.Research output: Contribution to journal › Article
TY - JOUR
T1 - Embedding black-box regression techniques into hierarchical Bayesian models
AU - Shaby, Benjamin A.
AU - Fink, Daniel
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Hierarchical models enable the encoding of a variety of parametric structures. However, when presented with a large number of covariates upon which some component of a model hierarchy depends, the modeller may be unwilling or unable to specify a form for that dependence. Data-mining methods are designed to automatically discover relationships between many covariates and a response surface, easily accommodating non-linearities and higher-order interactions. We present a method of wrapping hierarchical models around data-mining methods, preserving the best qualities of the two paradigms. We fit the resulting semi-parametric models using an approximate Gibbs sampler called HEBBRU. Using a simulated dataset, we show that HEBBRU is useful for exploratory analysis and displays excellent predictive accuracy. Finally, we apply HEBBRU to an ornithological dataset drawn from the eBird database.
AB - Hierarchical models enable the encoding of a variety of parametric structures. However, when presented with a large number of covariates upon which some component of a model hierarchy depends, the modeller may be unwilling or unable to specify a form for that dependence. Data-mining methods are designed to automatically discover relationships between many covariates and a response surface, easily accommodating non-linearities and higher-order interactions. We present a method of wrapping hierarchical models around data-mining methods, preserving the best qualities of the two paradigms. We fit the resulting semi-parametric models using an approximate Gibbs sampler called HEBBRU. Using a simulated dataset, we show that HEBBRU is useful for exploratory analysis and displays excellent predictive accuracy. Finally, we apply HEBBRU to an ornithological dataset drawn from the eBird database.
UR - http://www.scopus.com/inward/record.url?scp=84869148695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869148695&partnerID=8YFLogxK
U2 - 10.1080/00949655.2011.594052
DO - 10.1080/00949655.2011.594052
M3 - Article
AN - SCOPUS:84869148695
VL - 82
SP - 1753
EP - 1766
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
SN - 0094-9655
IS - 12
ER -