We discuss the interval-wise testing procedure: an inferential technique that can be applied to spatiotemporal data in order to test differences between groups of functional data defined either in time or space. IWT is based on the definition of an unadjusted and an adjusted p -value function that can be used to locally test a functional null hypothesis over the domain of functional data. When applied to spatiotemporal data, this technique can identify intervals of time or regions of space imputable for the rejection of a functional null hypothesis. The technique is illustrated and applied to a benchmark data set of Canadian temperatures, to test differences between different regions.

Pini, A., Vantini, S., Nonparametric Inference for Spatiotemporal Data Based on Local Null Hypothesis Testing for Functional Data, in Mateu, J., Giraldo, R. (ed.), Geostatistical Functional Data Analysis, Wiley, Straive, Chennai 2022: <<WILEY SERIES IN PROBABILITY>>, 242- 259. 10.1002/9781119387916.ch10 [http://hdl.handle.net/10807/203830]

Nonparametric Inference for Spatiotemporal Data Based on Local Null Hypothesis Testing for Functional Data

Pini, Alessia
;
2022

Abstract

We discuss the interval-wise testing procedure: an inferential technique that can be applied to spatiotemporal data in order to test differences between groups of functional data defined either in time or space. IWT is based on the definition of an unadjusted and an adjusted p -value function that can be used to locally test a functional null hypothesis over the domain of functional data. When applied to spatiotemporal data, this technique can identify intervals of time or regions of space imputable for the rejection of a functional null hypothesis. The technique is illustrated and applied to a benchmark data set of Canadian temperatures, to test differences between different regions.
2022
Inglese
Geostatistical Functional Data Analysis
9781119387848
Wiley
Pini, A., Vantini, S., Nonparametric Inference for Spatiotemporal Data Based on Local Null Hypothesis Testing for Functional Data, in Mateu, J., Giraldo, R. (ed.), Geostatistical Functional Data Analysis, Wiley, Straive, Chennai 2022: <<WILEY SERIES IN PROBABILITY>>, 242- 259. 10.1002/9781119387916.ch10 [http://hdl.handle.net/10807/203830]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/203830
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