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Fast moment‐based estimation for hierarchical models
Author(s) -
Perry Patrick O.
Publication year - 2017
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12165
Subject(s) - inference , computation , computer science , moment (physics) , hierarchical database model , scale (ratio) , estimation , maximum likelihood , computational complexity theory , estimation theory , algorithm , data mining , mathematical optimization , machine learning , artificial intelligence , statistics , mathematics , engineering , physics , systems engineering , classical mechanics , quantum mechanics
Summary 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. The paper proposes a moment‐based procedure for estimating the parameters of a hierarchical model which has its roots in a method originally introduced by Cochran in 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 with a standard maximum likelihood procedure, the method delivers competitive prediction performance while reducing the sequential computation time from hours to minutes.