Fast moment-based estimation for hierarchical models
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| Authors | Patrick O. Perry |
| Journal/Conference Name | Journal of the Royal Statistical Society: Series B (Statistical Methodology) |
| Paper Category | Other |
| Paper Abstract | Hierarchical models allow for heterogeneous behaviours in a population while simultaneously borrowing estimation strength across all subpopulations. Unfortunately, existing likelihood-based methods for fitting hierarchical models have high computational demands, and these demands have limited their adoption in large-scale prediction and inference problems. This paper proposes a moment-based procedure for estimating the parameters of a hierarchical model which has its roots in a method originally introduced by Cochin 1937. The method trades statistical efficiency for computational efficiency. It gives consistent parameter estimates, competitive prediction error performance, and substantial computational improvements. When applied to a large-scale recommender system application and compared to a standard maximum likelihood procedure, the method delivers competitive prediction performance while reducing the sequential computation time from hours to minutes. |
| Date of publication | 2015 |
| Code Programming Language | R |
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