Smoothed Censored Quantile Regression Process
We approach the globally-concerned censored quantile regression process with a smoothing mechanism for efficient computation. In the low dimensional regime, the regression process is formulated as solving a sequence of smoothed estimating equations (SEE), which can be done via a quasi-Newton method. Coordinatewise confidence intervals of coefficients can be constructed by multiplier bootstrap. In the high dimensional regime, the sparse learning problem is solved by iteratively reweighted ℓ1-regularized regression, and each ℓ1-regularized regression is solved by a local majorize-minimize algorithm.
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