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• W2049237551 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." @default.
• W2049237551 created "2016-06-24" @default.
• W2049237551 creator A5012771471 @default.
• W2049237551 creator A5044684896 @default.
• W2049237551 date "2012-01-01" @default.
• W2049237551 modified "2023-09-23" @default.
• W2049237551 title "Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data" @default.
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• W2049237551 doi "https://doi.org/10.1016/j.csda.2011.07.003" @default.
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