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ABSTRACT In this paper, we consider the weighted composite quantile regression for linear model with left-truncated data. The adaptive penalized procedure for variable selection is proposed. The asymptotic normality and… Click to show full abstract
ABSTRACT In this paper, we consider the weighted composite quantile regression for linear model with left-truncated data. The adaptive penalized procedure for variable selection is proposed. The asymptotic normality and oracle property of the resulting estimators are also established. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.
Keywords:
weighted composite;
left truncated;
regression;
composite quantile;
variable selection;
truncated data
Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2018
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Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2018
Link to full text (if available)
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recommendations!