Sparse wavelet estimation in quantile regression with multiple functional predictors
Bei Jiang and
Computational Statistics & Data Analysis, 2019, vol. 136, issue C, 12-29
To study quantile regression in partial functional linear model where response is scalar and predictors include both scalars and multiple functions, wavelet basis are usually adopted to better approximate functional slopes while effectively detect local features. The sparse group lasso penalty is imposed to select important functional predictors while capture shared information among them. The estimation problem can be reformulated into a standard second-order cone program and then solved by an interior point method. A novel algorithm is proposed by using alternating direction method of multipliers (ADMM) which was recently employed by many researchers in solving penalized quantile regression problems. The asymptotic properties such as the convergence rate and prediction error bound have been established. Simulations and a real data from ADHD-200 fMRI data are investigated to show the superiority of our proposed method.
Keywords: Functional data analysis; Sparse group lasso; ADMM; Convergence rate; Prediction error bound; ADHD (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:136:y:2019:i:c:p:12-29
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