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On mean derivative estimation of longitudinal and functional data: from sparse to dense
Statistical Papers ( IF 1.433 ) Pub Date : 2020-04-07 , DOI: 10.1007/s00362-020-01173-5
Hassan Sharghi Ghale-Joogh, S. Mohammad E. Hosseini-Nasab

Derivative estimation of the mean of longitudinal and functional data is useful, because it provides a quantitative measure of changes in the mean function that can be used for modeling of the data. We propose a general method for estimation of the derivative of the mean function that allows us to make inference about both longitudinal and functional data regardless of the sparsity of data. The \(L^2\) and uniform convergence rates of the local linear estimator for the true derivative of the mean function are derived. Then the optimal weighting scheme under the \(L^2\) rate of convergence is obtained. The performance of the proposed method is evaluated by a simulation study, and additionally compared with another existing method. The method is used to analyse a real data set involving children weight growth failure.
更新日期:2020-04-23

 

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