Name
Abstract | ||
|---|---|---|
Multi-level hierarchical models provide an attractive framework for incorporating correlations induced in a response variable organized in a hierarchy. Model fitting is challenging, especially for hierarchies with large number of nodes. We provide a novel algorithm based on a multi-scale Kalman filter that is both scalable and easy to implement. For non-Gaussian responses, quadratic approximation to the log-likelihood results in biased estimates. We suggest a bootstrap strategy to correct such biases. Our method is illustrated through simulation studies and analyses of real world data sets in health care and online advertising. |
Year | Venue | Keywords |
|---|---|---|
2008 | NIPS | hierarchical model |
Field | DocType | Citations |
Mathematical optimization,Data set,Computer science,Laplace's method,Kalman filter,Parametric statistics,Gaussian,Artificial intelligence,Estimation theory,Machine learning,Bootstrapping (electronics),Computation | Conference | 7 |
PageRank | References | Authors |
0.93 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Zhang | 1 | 138 | 10.45 |
| Deepak Agarwal | 2 | 11 | 3.05 |