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Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility

Summary: This article uses new statistical methods to estimate and classify service accessibility patterns for the financial services industry throughout the state of Georgia over a period of 16 years. The authors use a model-based method that creates a nonparametric clustering model of random functions that vary over time and are spatially interdependent. Assuming cluster membership represents a Markov random filed, the authors borrow information across functions to improve estimations of cluster membership and effects. A simulation study demonstrates the technique.

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  • Topics: Statistics
  • Keywords: Clustering, Data analysis, Financial industry, Modeling, Nonparametric methods, Parametric models, Service, Space-time modeling
  • Author: Jiang, Huijing; Serban, Nicoleta
  • Journal: Technometrics