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Poster
Fast Computation of Posterior Mode in Multi-Level Hierarchical Models
Deepak Agarwal · Liang Zhang

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @ None #None

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.

Author Information

Deepak Agarwal (LinkedIn)
Liang Zhang (Department of Statistical Science, Duke Unive)

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