Prior‐based model checking
Copyright policy )Prior‐based model checking
AbstractModel 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 Statistics46: 380–398; 2018 © 2018 Statistical Society of Canada
- University of Toronto Canada
- University of Sharjah United Arab Emirates
Dirichlet process, Methodology (stat.ME), FOS: Computer and information sciences, Bayesian inference, relative belief ratio, Nonparametric inference, model checking, Statistics - Methodology
Dirichlet process, Methodology (stat.ME), FOS: Computer and information sciences, Bayesian inference, relative belief ratio, Nonparametric inference, model checking, Statistics - Methodology
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).20 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!