Single-Index Quantile Regression with Left Truncated Data

XU Hongxia1,2, FAN Guoliang1, LI Jinchang2

1. 1. Department of Mathematics, Shanghai Maritime University, Shanghai 201306, China;
2. School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou 310018, China;
• Received:2021-04-15 Revised:2021-06-07 Online:2022-10-25 Published:2022-10-12
• Supported by:
This research was supported by the National Social Science Foundation of China under Grant No.21BTJ038.

XU Hongxia, FAN Guoliang, LI Jinchang. Single-Index Quantile Regression with Left Truncated Data[J]. Journal of Systems Science and Complexity, 2022, 35(5): 1963-1987.

The purpose of this paper is two fold.First,the authors investigate quantile regression (QR) estimation for single-index QR models when the response is subject to random left truncation.The random weights are introduced to deal with left truncated data and the associated iteration estimation method is proposed.The asymptotic properties for the proposed QR estimates of the index parameter and unknown link function are both obtained.Further,by combining the QR loss function and the adaptive LASSO penalization,a variable selection procedure for the index parameter is introduced and its oracle property is established.Second,a weighted empirical log-likelihood ratio of the index parameter based on the QR method is introduced and is proved to be asymptotic standard chi-square distribution.Furthermore,confidence regions of the index parameter can be constructed.The finite sample performance of the proposed methods are demonstrated.A real data analysis is also conducted to show the usefulness of the proposed approaches.
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