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Adding multiple functional and scalar covariates, model ({{EquationNote|2}}) can be extended to
{{NumBlk|::|<math display="block">Y = \sum_{k=1}^q Z_k\alpha_k + \sum_{j=1}^p \int_{\mathcal{T}_j} X_j^c(t) \beta_j(t) \,dt + \varepsilon,</math>|{{EquationRef|3}}}}
where <math>Z_1,\ldots,Z_q</math> are scalar covariates with <math>Z_1=1</math>, <math>\alpha_1,\ldots,\alpha_q</math> are regression coefficients for <math>Z_1,\ldots,Z_q</math>, respectively, <math>X^c_j</math> is a centered functional covariate given by <math>X_j^c(\cdot) = X_j(\cdot) - \mathbb{E}(X_j(\cdot))</math>, <math>\beta_j</math> is regression coefficient function for <math>X_j^c(\cdot)</math>, and <math>\mathcal{T}_j</math> is the ___domain of <math>X_j</math> and <math>\beta_j</math>, for <math>j=1,\ldots,p</math>. However, due to the parametric component <math>\alpha</math>, the estimation methods for model ({{EquationNote|2}}) cannot be used in this case<ref name=wang:16>{{cite journal|doi=10.1146/annurev-statistics-041715-033624|title=Functional Data Analysis|year=2016|last1=Wang|first1=Jane-Ling|last2=Chiou|first2=Jeng-Min|last3=Müller|first3=Hans-Georg|journal=[[Annual Review of Statistics and Its Application]]|volume=3|issue=1|pages=257–295|bibcode=2016AnRSA...3..257W|url=https://zenodo.org/record/895750|doi-access=free}}</ref> and alternative estimation methods for model ({{EquationNote|3}}) are available.<ref>{{Cite journal |last=Kong |first=Dehan |last2=Xue |first2=Kaijie |last3=Yao |first3=Fang |last4=Zhang |first4=Hao H. |date= |title=Partially functional linear regression in high dimensions |url=https://academic.oup.com/biomet/article-lookup/doi/10.1093/biomet/asv062 |journal=Biometrika |language=en |volume=103 |issue=1 |pages=147–159 |doi=10.1093/biomet/asv062 |issn=0006-3444|url-access=subscription }}</ref><ref>{{Cite journal |last=Hu |first=Z. |date=2004-06-01 |title=Profile-kernel versus backfitting in the partially linear models for longitudinal/clustered data |url=https://academic.oup.com/biomet/article-lookup/doi/10.1093/biomet/91.2.251 |journal=Biometrika |language=en |volume=91 |issue=2 |pages=251–262 |doi=10.1093/biomet/91.2.251 |issn=0006-3444|url-access=subscription }}</ref>
=== Functional linear models with functional responses ===
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Assuming that <math>\mathcal{S} = \mathcal{T}</math>, another model, known as the functional concurrent model, sometimes also referred to as the varying-coefficient model, is of the form
{{NumBlk|::|<math display="block">Y(t) = \alpha_0(t) + \alpha(t)X(t)+\varepsilon(t),\ \text{for}\ t\in\mathcal{T},</math>|{{EquationRef|6}}}}
where <math>\alpha_0</math> and <math>\alpha</math> are coefficient functions. Note that model ({{EquationNote|6}}) assumes the value of <math>Y</math> at time <math>t</math>, i.e., <math>Y(t)</math>, only depends on that of <math>X</math> at the same time, i.e., <math>X(t)</math>. Various estimation methods can be applied to model ({{EquationNote|6}}).<ref>{{Cite journal |last=Fan |first=Jianqing |last2=Zhang |first2=Wenyang |date= |title=Statistical estimation in varying coefficient models |url=https://projecteuclid.org/journals/annals-of-statistics/volume-27/issue-5/Statistical-estimation-in-varying-coefficient-models/10.1214/aos/1017939139.full |journal=The Annals of Statistics |volume=27 |issue=5 |pages=1491–1518 |doi=10.1214/aos/1017939139 |issn=0090-5364}}</ref><ref>{{Cite journal |last=Huang |first=Jianhua Z. |last2=Wu |first2=Colin O. |last3=Zhou |first3=Lan |date=2004 |title=Polynomial Spline Estimation and Inference for Varying Coefficient Models with Longitudinal Data |url=https://www.jstor.org/stable/24307415 |journal=Statistica Sinica |volume=14 |issue=3 |pages=763–788 |issn=1017-0405}}</ref><ref>{{Cite journal |last=Şentürk |first=Damla |last2=Müller |first2=Hans-Georg |date=2010-09-01 |title=Functional Varying Coefficient Models for Longitudinal Data |url=https://www.tandfonline.com/doi/abs/10.1198/jasa.2010.tm09228 |journal=Journal of the American Statistical Association |doi=10.1198/jasa.2010.tm09228 |issn=0162-1459|url-access=subscription }}</ref><br />
Adding multiple functional covariates, model ({{EquationNote|6}}) can also be extended to
<math display="block">Y(t) = \alpha_0(t) + \sum_{j=1}^p\alpha_j(t)X_j(t)+\varepsilon(t),\ \text{for}\ t\in\mathcal{T},</math>
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