• Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses

    2 monthes ago - By Springer

    Abstract
    The paper concerns the regularized quantile regression for ultrahigh-dimensional data with responses missing not at random. The propensity score is specified by the semiparametric exponential tilting model. We use the Pearson Chi-square type test statistic for identification of the important features in the sparse propensity score model, and employ the adjusted empirical likelihood method for estimation of the parameters in the reduced model. With the estimated propensity score model, we suggest an inverse probability weighted and penalized objective function for regularized...
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