Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data

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:

• Asymptotic normality
• composite quantile regression
• left-truncated data
• oracle property
• variable selection.

MATHEMATICS SUBJECT CLASSIFICATION:

• 62N02
• 62E20

Acknowledgments

The authors thank the reviewers for their careful reading and valuable comments.

Additional information

Funding

The work was supported by Projects of the National Social Science Foundation of China (16BTJ029).