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

  • Polytechnic University of Milan

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationGeostatistical Functional Data Analysis
EditorsJ. Mateu, R. Giraldo
Pages242-259
Number of pages18
DOIs
Publication statusPublished - 2022

Publication series

NameWILEY SERIES IN PROBABILITY

Keywords

  • Canadian climate
  • interval wise testing
  • nonparametric inference
  • null hypothesis significance testing

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