Multiple change point clustering of count processes with application to California COVID data
Pattern Recognition Letters
; 2022.
Article
in English
| ScienceDirect | ID: covidwho-1763924
ABSTRACT
In this paper, a model-based clustering algorithm relying on a finite mixture of negative binomial Lévy processes is proposed. The algorithm models heterogeneous stochastic count process data and automatically estimates multiple change points upon fitting the mixture model. Such change point estimation identifies time points when deviation from the standard process has occurred and serves as an important diagnostic tool for analyzing temporal data. The proposed model is applied to the COVID-positive ICU cases in the state of California with very interesting results.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Language:
English
Journal:
Pattern Recognition Letters
Year:
2022
Document Type:
Article
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