An FDA-Based Approach for Clustering Elicited Expert Knowledge
| Autor: | Andrew Zamecnik, Francisco Torres-Avilés, Juan Carlos Correa, Fernando Marmolejo-Ramos, Carlos Barrera-Causil |
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| Přispěvatelé: | Barrera-Causil, Carlos, Correa, Juan, Zamecnik, Andrew, Torres-Avilés, Francisco, Marmolejo-Ramos, Fernando |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Hellinger distance
Computer science 02 engineering and technology Bayesian inference 01 natural sciences 010104 statistics & probability expert knowledge elicitation 0202 electrical engineering electronic engineering information engineering 0101 mathematics Cluster analysis lcsh:Statistics lcsh:HA1-4737 functional data analysis Structure (mathematical logic) business.industry Functional data analysis Pattern recognition Hierarchical clustering Probability distribution Nesting (computing) 020201 artificial intelligence & image processing Artificial intelligence business hierarchical clustering |
| Zdroj: | Stats, Vol 4, Iss 14, Pp 184-204 (2021) Stats Volume 4 Issue 1 Pages 14-204 |
| Popis: | Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets Refereed/Peer-reviewed |
| Databáze: | OpenAIRE |
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