Inference for the Evolution in Series of Studies
| dc.contributor.author | Areia, A. | |
| dc.contributor.author | Mexia, J. | |
| dc.contributor.author | Oliveira, Maria | |
| dc.date.accessioned | 2019-03-25T17:30:00Z | |
| dc.date.available | 2019-03-25T17:30:00Z | |
| dc.date.issued | 2018-07-15 | |
| dc.description.abstract | Studies will be matrix triplets (X,Dp,Dn), where the matrix X has a row per object and a column per variable, while Dp and Dn are weight matrices for objects and variables, respectively. Given a series of studies (Xi,Dp,Dn),i=1,…,k, we condense the matrix triplets into the , and use spectral analysis of matrix [ ] with ( ) to study the series evolution. When we have a series of studies for each treatment of a basis design we carry out an ANOVA-like inference to study the action of the factors in the base design on the evolution of the series associated to the differents treatments. | por |
| dc.identifier.authoremail | anibal.areia@esce.ips.pt | |
| dc.identifier.authoremail | jtm@fct.unl.pt | |
| dc.identifier.authoremail | mmo@uevora.pt | |
| dc.identifier.citation | Areia, A., Mexia, J., Oliveira, M., 2018. Inference for the Evolution in Series of Studies.Proceedings of 2018 International Conference on Mathematics and Statistics (ICoMS 2018). 55-58. ACM ISBN: 978-1-4503-6538-3. | por |
| dc.identifier.isbn | 978-1-4503-6538-3 | |
| dc.identifier.scientificarea | 336 | por |
| dc.identifier.uri | http://hdl.handle.net/10174/25427 | |
| dc.language.iso | por | por |
| dc.peerreviewed | yes | por |
| dc.publisher | The Association for Computing Machinery 2 Penn Plaza, Suite 701 New York New York 10121-0701 | por |
| dc.rights | restrictedAccess | por |
| dc.subject | ANOVA | por |
| dc.subject | Inference | por |
| dc.subject | STATIS | por |
| dc.title | Inference for the Evolution in Series of Studies | por |
| dc.type | article | por |
| degois.publication.firstPage | 55 | por |
| degois.publication.lastPage | 58 | por |
| rcaap.description.embargofct | ACM COPYRIGHT NOTICE. Copyright © 2018 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. | por |