Sparse ridge sliced inverse quantile regression without quantile crossing
Author
Source
al-Qadisiya Journal For Administrative and Economic Sciences
Issue
Vol. 18, Issue 1 (31 Mar. 2016), pp.28-47, 20 p.
Publisher
University of al-Qadisiyah College of Administration and Economics
Publication Date
2016-03-31
Country of Publication
Iraq
No. of Pages
20
Main Subjects
Abstract EN
Quantile regression provides a more complete statistical analysis of the stochastic relationships between random variables.
While this technique has become very popular as a comprehensive extension of the classical mean regression it nonetheless suffers the problem of crossing of regression functions estimated at different orders of quantiles.
Theoretically, the extension of conditional quantiles to higher dimension of X is straight forward.
However, its practical success suffers from the socalled ‘curse of dimensionality’.
In this article we propose a method of obtaining quantile regression estimates for high dimension data without the unfavourable quality of quantile crossing.
The proposed method is a two step procedure that initially employs sparse ridge sliced inverse regression (SRSIR) to achieve dimension reduction when the predictors are possibly correlated and then followed by the usage of non-parametric method to estimate non-crossing quantile regression.
For the second stage of our method we employ double kernel smoothing method (Yu and Jones,1998); monotone-based smoothing method based on the convolution of the distribution (Dette and Volgushev,2008) and joint non-crossing quantile smoothing spline method (Bondell et al., 2010) for estimating the conditional quantile without quantile crossing.
Through a simulation and empirical study we compare our estimators with that of Gannon et al.
(2004).
American Psychological Association (APA)
al-Kinani, Ali Jawad Kazim. 2016. Sparse ridge sliced inverse quantile regression without quantile crossing. al-Qadisiya Journal For Administrative and Economic Sciences،Vol. 18, no. 1, pp.28-47.
https://search.emarefa.net/detail/BIM-797819
Modern Language Association (MLA)
al-Kinani, Ali Jawad Kazim. Sparse ridge sliced inverse quantile regression without quantile crossing. al-Qadisiya Journal For Administrative and Economic Sciences Vol. 18, no. 1 (2016), pp.28-47.
https://search.emarefa.net/detail/BIM-797819
American Medical Association (AMA)
al-Kinani, Ali Jawad Kazim. Sparse ridge sliced inverse quantile regression without quantile crossing. al-Qadisiya Journal For Administrative and Economic Sciences. 2016. Vol. 18, no. 1, pp.28-47.
https://search.emarefa.net/detail/BIM-797819
Data Type
Journal Articles
Language
English
Record ID
BIM-797819