Abstract
In the framework of null hypothesis significance testing for functional data, we propose a procedure able to select intervals of the domain imputable for the rejection of a null hypothesis. An unadjusted p-value function and an adjusted one are the output of the procedure, namely interval-wise testing. Depending on the sort and level α of type-I error control, significant intervals can be selected by thresholding the two p-value functions at level α. We prove that the unadjusted (adjusted) p-value function point-wise (interval-wise) controls the probability of type-I error and it is point-wise (interval-wise) consistent. To enlighten the gain in terms of interpretation of the phenomenon under study, we applied the interval-wise testing to the analysis of a benchmark functional data set, i.e. Canadian daily temperatures. The new procedure provides insights that current state-of-the-art procedures do not, supporting similar advantages in the analysis of functional data with less prior knowledge.
Lingua originale | English |
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pagine (da-a) | 407-424 |
Numero di pagine | 18 |
Rivista | Journal of Nonparametric Statistics |
Volume | 29 |
DOI | |
Stato di pubblicazione | Pubblicato - 2017 |
Keywords
- Inference
- Statistics and Probability
- Statistics, Probability and Uncertainty
- canadian temperatures
- domain selection
- functional data