Function-on-function regression for two-dimensional functional data

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We present methods for modeling and estimation of a concurrent functional regression when the predictors and responses are two-dimensional functional datasets. The implementations use spline basis functions and model fitting is based on smoothing penalties and mixed model estimation. The proposed methods are implemented in available statistical software, allow the construction of confidence intervals for the bivariate model parameters, and can be applied to completely or sparsely sampled responses. Methods are tested to data in simulations and they show favorable results in practice. The usefulness of the methods is illustrated in an application to environmental data.

Original languageEnglish
Pages (from-to)2656-2669
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume47
Issue number9
DOIs
StatePublished - 21 Oct 2018

Fingerprint

Functional Data
Regression Function
Splines
Statistical Software
Spline Functions
Model Fitting
Mixed Model
Confidence interval
Basis Functions
Penalty
Smoothing
Concurrent
Predictors
Regression
Modeling
Simulation

Keywords

  • Bivariate
  • Functional data analysis
  • Functional regression
  • Penalized splines
  • Smoothing

Cite this

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Function-on-function regression for two-dimensional functional data. / Ivanescu, Andrada.

In: Communications in Statistics: Simulation and Computation, Vol. 47, No. 9, 21.10.2018, p. 2656-2669.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Function-on-function regression for two-dimensional functional data

AU - Ivanescu, Andrada

PY - 2018/10/21

Y1 - 2018/10/21

N2 - We present methods for modeling and estimation of a concurrent functional regression when the predictors and responses are two-dimensional functional datasets. The implementations use spline basis functions and model fitting is based on smoothing penalties and mixed model estimation. The proposed methods are implemented in available statistical software, allow the construction of confidence intervals for the bivariate model parameters, and can be applied to completely or sparsely sampled responses. Methods are tested to data in simulations and they show favorable results in practice. The usefulness of the methods is illustrated in an application to environmental data.

AB - We present methods for modeling and estimation of a concurrent functional regression when the predictors and responses are two-dimensional functional datasets. The implementations use spline basis functions and model fitting is based on smoothing penalties and mixed model estimation. The proposed methods are implemented in available statistical software, allow the construction of confidence intervals for the bivariate model parameters, and can be applied to completely or sparsely sampled responses. Methods are tested to data in simulations and they show favorable results in practice. The usefulness of the methods is illustrated in an application to environmental data.

KW - Bivariate

KW - Functional data analysis

KW - Functional regression

KW - Penalized splines

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