Block testing in covariance and precision matrices for functional data analysis

Marie Morvan; Alessia Pini; Madison Giacofci; Valerie Monbet

Block testing in covariance and precision matrices for functional data analysis

Marie Morvan, Alessia Pini^*, Madison Giacofci, Valerie Monbet

^*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in libro › Contributo a convegno

Abstract

We propose a method to test dependence or conditional dependence be- tween parts of the domain of functional data. The tests are based on permutation procedure that tests if suitable blocks of the covariance or precision matrix of ba- sis expansion coefficients are equal to zero. We show that the procedure is able to identify the true structure of conditional dependence.

Lingua originale	English
Titolo della pubblicazione ospite	Book of short papers SIS 2021
Pagine	911-916
Numero di pagine	6
Stato di pubblicazione	Pubblicato - 2021
Evento	SIS 2021 - Pisa Durata: 21 giu 2021 → 25 giu 2021

Convegno

Convegno	SIS 2021
Città	Pisa
Periodo	21/6/21 → 25/6/21

Keywords

functional data analysis, independence, conditional independence

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title = "Block testing in covariance and precision matrices for functional data analysis",

abstract = "We propose a method to test dependence or conditional dependence be- tween parts of the domain of functional data. The tests are based on permutation procedure that tests if suitable blocks of the covariance or precision matrix of ba- sis expansion coefficients are equal to zero. We show that the procedure is able to identify the true structure of conditional dependence.",

keywords = "functional data analysis, independence, conditional independence, functional data analysis, independence, conditional independence",

author = "Marie Morvan and Alessia Pini and Madison Giacofci and Valerie Monbet",

year = "2021",

language = "English",

isbn = "9788891927361",

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note = "SIS 2021 ; Conference date: 21-06-2021 Through 25-06-2021",

}

TY - GEN

T1 - Block testing in covariance and precision matrices for functional data analysis

AU - Morvan, Marie

AU - Pini, Alessia

AU - Giacofci, Madison

AU - Monbet, Valerie

PY - 2021

Y1 - 2021

N2 - We propose a method to test dependence or conditional dependence be- tween parts of the domain of functional data. The tests are based on permutation procedure that tests if suitable blocks of the covariance or precision matrix of ba- sis expansion coefficients are equal to zero. We show that the procedure is able to identify the true structure of conditional dependence.

AB - We propose a method to test dependence or conditional dependence be- tween parts of the domain of functional data. The tests are based on permutation procedure that tests if suitable blocks of the covariance or precision matrix of ba- sis expansion coefficients are equal to zero. We show that the procedure is able to identify the true structure of conditional dependence.

KW - functional data analysis, independence, conditional independence

UR - http://hdl.handle.net/10807/203845

M3 - Conference contribution

SN - 9788891927361

SP - 911

EP - 916

BT - Book of short papers SIS 2021

T2 - SIS 2021

Y2 - 21 June 2021 through 25 June 2021

ER -

Block testing in covariance and precision matrices for functional data analysis

Abstract

Convegno

Keywords

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