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Multi-aspect local inference for functional data: Analysis of ultrasound tongue profiles

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  • Pini, Alessia
  • Spreafico, Lorenzo
  • Vantini, Simone
  • Vietti, Alessandro

Abstract

Motivated by the analysis of a dataset of ultrasound tongue profiles, we present multi-aspect interval-wise testing (IWT), i.e., a local nonparametric inferential technique for functional data embedded in Sobolev spaces. Multi-aspect IWT is a nonparametric procedure that tests differences between groups of functional data, jointly taking into account the curves and their derivatives. Multi-aspect IWT provides adjusted multi-aspect p-value functions that can be used to select intervals of the domain that are imputable for the rejection of a null hypothesis. As a result, it can impute the rejection of a functional null hypothesis to specific intervals of the domain and to specific orders of differentiation. We show that the multi-aspect p-value functions are provided with a control of the family-wise error rate and that they are consistent. We apply multi-aspect IWT to the analysis of a dataset of tongue profiles recorded for a study on Tyrolean, a German dialect spoken in South Tyrol. We test differences between five different ways of articulating the uvular /r/: vocalized /r/, approximant, fricative, tap, and trill. Multi-aspect IWT-based comparisons result in an informative and detailed representation of the regions of the tongue where a significant difference occurs.

Suggested Citation

  • Pini, Alessia & Spreafico, Lorenzo & Vantini, Simone & Vietti, Alessandro, 2019. "Multi-aspect local inference for functional data: Analysis of ultrasound tongue profiles," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 162-185.
    • Handle: RePEc:eee:jmvana:v:170:y:2019:i:c:p:162-185
    DOI: 10.1016/j.jmva.2018.11.006
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    1. Todd Colin Pataky & Konrad Abramowicz & Dominik Liebl & Alessia Pini & Sara Sjöstedt Luna & Lina Schelin, 2023. "Simultaneous inference for functional data in sports biomechanics," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 369-392, March.
    2. Pini, Alessia & Sørensen, Helle & Tolver, Anders & Vantini, Simone, 2023. "Local inference for functional linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    3. Hassan Sharghi Ghale-Joogh & S. Mohammad E. Hosseini-Nasab, 2021. "On mean derivative estimation of longitudinal and functional data: from sparse to dense," Statistical Papers, Springer, vol. 62(4), pages 2047-2066, August.
    4. Kraus, David, 2019. "Inferential procedures for partially observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 583-603.
    5. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.

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