Optimal Estimation of Large Functional and Longitudinal Data by Using Functional Linear Mixed Model

Mengfei Ran and Yihe Yang ()
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Mengfei Ran: Graduate School of Engineering Science, Osaka University, Osaka 560-0043, Japan
Yihe Yang: Department of Population and Quantitative Health Science, Case Western Reserve University, Cleveland, OH 44106, USA

Mathematics, 2022, vol. 10, issue 22, 1-28

Abstract: The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the most challenging problems in the field of high-dimensional statistics. Functional Principal Components Analysis (FPCA) and Functional Linear Mixed Model (FLMM) are two major statistical tools used to address the estimation of large functional and longitudinal data; however, the former suffers from a dramatically increasing computational burden while the latter does not have clear asymptotic properties. In this paper, we propose a computationally effective estimator of large functional and longitudinal data within the framework of FLMM, in which all the parameters can be automatically estimated. Under certain regularity assumptions, we prove that the mean function estimation and individual trajectory prediction reach the minimax lower bounds of all nonparametric estimations. Through numerous simulations and real data analysis, we show that our new estimator outperforms the traditional FPCA in terms of mean function estimation, individual trajectory prediction, variance estimation, covariance function estimation, and computational effectiveness.

Keywords: functional data analysis; functional linear mixed model; functional principal components analysis; longitudinal data analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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