Analysis of multivariate non-gaussian functional data: A semiparametric latent process approach

Jiakun Jiang, Huazhen Lin, Qingzhi Zhong and Yi Li

Journal of Multivariate Analysis, 2022, vol. 189, issue C

Abstract: Commonly assumed for multivariate functional regression models are normality and structural dependence, which, however, may not hold in practice. To relax these restrictions, we propose a new semiparametric transformation latent process functional regression model for multivariate functional data. Our model does not require normality assumptions or any specific dependence structures among multivariate response curves or intra-individual variability across time. We propose a combined likelihood- and estimating equation-based method to estimate parameters, transformation functions and covariance structures. We establish theoretical properties, including n−consistency and asymptotic normality, for the proposed estimators. The utility of the method is illustrated via extensive simulations and analyses of an elderly cognitive evolution dataset, which yield a better fit than the other competing methods and some interesting findings.

Keywords: Functional regression analysis; Latent process; Normal transformation model; Semi-parametric (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:189:y:2022:i:c:s0047259x21001664

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DOI: 10.1016/j.jmva.2021.104888

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