This paper proposes a spatial functional formulation of the normal mixed effect model for the statistical classification of spatially dependent Gaussian curves. Fixed effect parameters are represented in terms of a functional mul- tiple regression model whose regression operators can change in space. Local spatial homogeneity of these operators is measured in terms of their Hilbert- Schmidt distances, leading to the classification of fixed effect curves in dif- ferent groups. A real data example in the financial context is analyzed as an illustration.

A Spatial functional Approach for Curve Classification A Spanish firms’ panel data analysis

ROMANO, Elvira
2014

Abstract

This paper proposes a spatial functional formulation of the normal mixed effect model for the statistical classification of spatially dependent Gaussian curves. Fixed effect parameters are represented in terms of a functional mul- tiple regression model whose regression operators can change in space. Local spatial homogeneity of these operators is measured in terms of their Hilbert- Schmidt distances, leading to the classification of fixed effect curves in dif- ferent groups. A real data example in the financial context is analyzed as an illustration.
2014
978-88-8467-874-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/158630
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