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Prior‐based model checking
Author(s) -
AlLabadi Luai,
Evans Michael
Publication year - 2018
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11457
Subject(s) - hyperparameter , dirichlet distribution , selection (genetic algorithm) , hierarchical dirichlet process , computer science , process (computing) , dirichlet process , model selection , base (topology) , machine learning , statistics , mathematical optimization , econometrics , algorithm , artificial intelligence , mathematics , latent dirichlet allocation , topic model , inference , mathematical analysis , boundary value problem , operating system
Model checking procedures are considered based on the use of the Dirichlet process and relative belief. This combination is seen to lead to some unique advantages for this problem. Of considerable importance is the selection of the hyperparameters for the Dirichlet process. A particular choice is advocated here for the base distribution that avoids prior‐data conflict and double use of the data, while the choice of the concentration parameter is based on elicitation. Several examples are presented in which the proposed approach exhibits excellent performance. The Canadian Journal of Statistics 46: 380–398; 2018 © 2018 Statistical Society of Canada