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Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data

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  • Wu, Tong Tong
  • He, Xin

Abstract

This paper explores a fast algorithm to select relevant predictors for the response process with panel count data. Based on the lasso penalized pseudo-objective function derived from an estimating equation, the coordinate ascent accelerates the estimation of regression coefficients. The coordinate ascent algorithm is capable of selecting relevant predictors for underdetermined problems where the number of predictors far exceeds the number of cases. It relies on a tuning constant that can be chosen by generalized cross-validation. Our tests on simulated and real data demonstrate the virtue of penalized regression in model building and prediction for panel count data in ultrahigh-dimensional settings.

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  • Wu, Tong Tong & He, Xin, 2012. "Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 25-33, January.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:1:p:25-33
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    References listed on IDEAS

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    1. P. Tseng, 2001. "Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization," Journal of Optimization Theory and Applications, Springer, vol. 109(3), pages 475-494, June.
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    9. Xingwei Tong & Xin He & Liuquan Sun & Jianguo Sun, 2009. "Variable Selection for Panel Count Data via Non‐Concave Penalized Estimating Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 620-635, December.
    10. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. Haiying Wang & Yang Li & Jianguo Sun, 2015. "Focused and Model Average Estimation for Regression Analysis of Panel Count Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 732-745, September.

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