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

^*Corresponding author

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Book of short papers SIS 2021
Pages	911-916
Number of pages	6
Publication status	Published - 2021
Event	SIS 2021 - Pisa Duration: 21 Jun 2021 → 25 Jun 2021

Conference

Conference	SIS 2021
City	Pisa
Period	21/6/21 → 25/6/21

Keywords

functional data analysis, independence, conditional independence

Cite this

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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",

pages = "911--916",

booktitle = "Book of short papers SIS 2021",

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

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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

Conference

Keywords

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Cite this