Predictive functional linear models with diverging number of semiparametric single-index interactions

Liu, Y; Li, Y; Carroll, RJ; Wang, N

Predictive functional linear models with diverging number of semiparametric single-index interactions

Liu, Y Li, Y Carroll, RJ Wang, N

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Econometrics, 2022, 230, (2), pp. 221-239
Issue Date:: 2022-01-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0304407621001020-main.pdf		698.2 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Li, Y
dc.contributor.author	Carroll, RJ
dc.contributor.author	Wang, N
dc.date.accessioned	2022-12-02T01:18:05Z
dc.date.available	2022-12-02T01:18:05Z
dc.date.issued	2022-01-01
dc.identifier.citation	Journal of Econometrics, 2022, 230, (2), pp. 221-239
dc.identifier.issn	0304-4076
dc.identifier.issn	1872-6895
dc.identifier.uri	http://hdl.handle.net/10453/164039
dc.description.abstract	When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor's dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a sequence of semiparametric working models with a diverging number of predictors, which are FPCA scores with estimation errors. We show that the parametric component of the model is root-n consistent and asymptotically normal, the overall prediction error is dominated by the estimation of the nonparametric interaction function, and justify a CV-based procedure to select the tuning parameters.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation	National Cancer InstituteU01CA057030
dc.relation.ispartof	Journal of Econometrics
dc.relation.isbasedon	10.1016/j.jeconom.2021.03.010
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1402 Applied Economics, 1403 Econometrics
dc.subject.classification	Econometrics
dc.title	Predictive functional linear models with diverging number of semiparametric single-index interactions
dc.type	Journal Article
utslib.citation.volume	230
utslib.location.activity	Netherlands
utslib.for	0104 Statistics
utslib.for	1402 Applied Economics
utslib.for	1403 Econometrics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-12-02T01:18:04Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	230
utslib.citation.issue	2

Abstract:

When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor's dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a sequence of semiparametric working models with a diverging number of predictors, which are FPCA scores with estimation errors. We show that the parametric component of the model is root-n consistent and asymptotically normal, the overall prediction error is dominated by the estimation of the nonparametric interaction function, and justify a CV-based procedure to select the tuning parameters.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/164039