Quadratic regression for functional response models

Hidetoshi Matsui

Econometrics and Statistics, 2020, vol. 13, issue C, 125-136

Abstract: A problem of constructing a regression model with a functional predictor and a functional response is considered. A functional quadratic model is an extension of a functional linear model and includes the quadratic term that takes the interaction between two different time points of the functional data into consideration. Predictor and the coefficient functions in the model are supposed to be expressed by basis expansions, and then parameters included in the model are estimated by the penalized likelihood method assuming that the error function follows a Gaussian process. Monte Carlo simulations are conducted to illustrate the efficacy of the proposed method. Finally, the proposed method is applied to the analysis of meteorological data and the results are explored.

Keywords: Functional data analysis; Gaussian process; Interaction; Model selection (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:13:y:2020:i:c:p:125-136

DOI: 10.1016/j.ecosta.2018.12.003

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