Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data

By Ming-Yen Cheng, Toshio Honda, Jialiang Li, Heng Peng. 2013

76 citations recorded by the source corpus.

Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for finding a sparse semivarying coefficient model, which is widely accepted for longitudinal data analysis. Our proposed method first reduces the number of covariates to a moderate order by employing a screening procedure, and then identifies both the varying and constant coefficients using a group SCAD estimator, which is subsequently refined by accounting for the within-subject correlation. The screening procedure is based on working independence and B-spline marginal models. Under weaker conditions than those in the literature, we show that with high probability only irrelevant variables will be screened out, and the number of selected variables can be bounded by a moderate order. This allows the desirable sparsity and oracle properties of the subsequent structure identification step. Note that existing methods require some kind of iterative screening in order to achieve this, thus they demand heavy computational effort and consistency is not guaranteed. The refined semivarying coefficient model employs profile least squares, local linear smoothing and nonparametric covariance estimation, and is semiparametric efficient. We also suggest ways to implement the proposed methods, and to select the tuning parameters. An extensive simulation study is summarized to demonstrate its finite sample performance and the yeast cell cycle data is analyzed.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/83e0dd05e432e274b26a31720e60f6a04ad8ad71

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Citations in the corpus

Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series (2018, background): “However, the existing literature usually considers the case where the observations are either independent or collected from correlated and sparse longitudinal data (see Cheng et al. 2014), which rules out the nonlinear dynamic time series setting (over a long time span).”