Bayesian Models for Registration of Functional Data

Jacopo Gardella; Rossella Carone; Simone Ciardulli; Nicola Francescon; Matteo Freddi; Elisa Garlanda; Alessandro Casa; Alessia Pini

doi:10.1007/978-3-031-64431-3_59

Bayesian Models for Registration of Functional Data

Jacopo Gardella
, Rossella Carone
, Simone Ciardulli
, Nicola Francescon
, Matteo Freddi
, Elisa Garlanda
, Alessandro Casa
, Alessia Pini

Risultato della ricerca: Contributo in libro › Contributo a conferenza

Abstract

Studies involving functional data frequently require curve registration, i.e. the alignment of salient features in the temporal domain, as a preliminary step before the application of inferential techniques. Through this procedure, phase variability becomes negligible, thus allowing to focus solely on amplitude variability. This work introduces a Bayesian model for curve alignment and applies it to a biomechanical dataset with three groups of patients.

Lingua originale	Inglese
Titolo della pubblicazione ospite	Methodological and Applied Statistics and Demography III
Editore	Springer
Pagine	354-359
Numero di pagine	6
ISBN (stampa)	9783031644306
DOI	https://doi.org/10.1007/978-3-031-64431-3_59
Stato di pubblicazione	Pubblicato - 2025

Keywords

B-splines
Bayesian warping
Functional data
Markov chain Monte Carlo

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10.1007/978-3-031-64431-3_59

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title = "Bayesian Models for Registration of Functional Data",

abstract = "Studies involving functional data frequently require curve registration, i.e. the alignment of salient features in the temporal domain, as a preliminary step before the application of inferential techniques. Through this procedure, phase variability becomes negligible, thus allowing to focus solely on amplitude variability. This work introduces a Bayesian model for curve alignment and applies it to a biomechanical dataset with three groups of patients.",

keywords = "B-splines, Bayesian warping, Functional data, Markov chain Monte Carlo, B-splines, Bayesian warping, Functional data, Markov chain Monte Carlo",

author = "Jacopo Gardella and Rossella Carone and Simone Ciardulli and Nicola Francescon and Matteo Freddi and Elisa Garlanda and Alessandro Casa and Alessia Pini",

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AU - Gardella, Jacopo

AU - Carone, Rossella

AU - Ciardulli, Simone

AU - Francescon, Nicola

AU - Freddi, Matteo

AU - Garlanda, Elisa

AU - Casa, Alessandro

AU - Pini, Alessia

PY - 2025

Y1 - 2025

N2 - Studies involving functional data frequently require curve registration, i.e. the alignment of salient features in the temporal domain, as a preliminary step before the application of inferential techniques. Through this procedure, phase variability becomes negligible, thus allowing to focus solely on amplitude variability. This work introduces a Bayesian model for curve alignment and applies it to a biomechanical dataset with three groups of patients.

AB - Studies involving functional data frequently require curve registration, i.e. the alignment of salient features in the temporal domain, as a preliminary step before the application of inferential techniques. Through this procedure, phase variability becomes negligible, thus allowing to focus solely on amplitude variability. This work introduces a Bayesian model for curve alignment and applies it to a biomechanical dataset with three groups of patients.

KW - B-splines

KW - Bayesian warping

KW - Functional data

KW - Markov chain Monte Carlo

KW - B-splines

KW - Bayesian warping

KW - Functional data

KW - Markov chain Monte Carlo

UR - https://publicatt.unicatt.it/handle/10807/313061

U2 - 10.1007/978-3-031-64431-3_59

DO - 10.1007/978-3-031-64431-3_59

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SN - 9783031644306

SP - 354

EP - 359

BT - Methodological and Applied Statistics and Demography III

PB - Springer

ER -

Bayesian Models for Registration of Functional Data

Abstract

Keywords

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