High leverage detection in general functional regression models with spatially correlated errors
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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INVESTIGACIONMetadata
Title
High leverage detection in general functional regression models with spatially correlated errorsDate
2021Publisher
WileyISSN
1524-1904; 1526-4025Bibliographic citation
Romano E, Giraldo R, Mateu J, Diana A. High leverage detection in general functional regression models with spatially correlated errors. Appl Stochastic Models Bus Ind. 2021;1-13. doi: 10.1002/asmb.2654Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
The presence of curves that deviate markedly from the core of a set of curves can
greatly affect inference and forecasting in a functional regression model. Thus
their detection is key to increase the accuracy of ... [+]
The presence of curves that deviate markedly from the core of a set of curves can
greatly affect inference and forecasting in a functional regression model. Thus
their detection is key to increase the accuracy of the required estimates. This
work introduces the concepts of high leverage in general functional regression
models with independent and spatially correlated errors. The projection matrix,
also known as Hat matrix, plays a crucial role in classical model diagnosis, since
it provides a measure of leverage. We propose a generalisation of the projection matrix in both the functional and the spatial functional frameworks under
two settings, when the response variable is a scalar, and when it is a function
itself, the so-called total model. Commonly used influence measures are also
proposed as functions of the generalised functional leverages and residuals. An
application of the proposed procedures for investigating the effect of outliers on
the relationship between transformation of the banking industry and the size of
cooperative banks in Italy over a period of 14 years is presented. [-]
Is part of
Applied Stochastic Models in Business and Industry. 2021;1-13Rights
© 2021 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons Ltd
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info:eu-repo/semantics/openAccess
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