Lasso Quantile Principal Component Regression

Authors

  • Mohammed H. Al-Sharoot Department of Statistics, College of Administration and Economics University of AL-Qadisiyah, IRAQ
  • Fatimah K. Mohammed Education Directorate Babylon, Ministry of Eduucation, IRAQ
  • Hameedah N. Mayali cDepartment of Statistics, College of Administration and Economics University of AL-Qadisiyah, IRAQ

DOI:

https://doi.org/10.29304/jqcsm.2023.15.41356

Keywords:

Quantile Regression, principal component, lasso

Abstract

The classical regression model is very sensitive to econometrics problems, one this econometrics problem is Multicollinearity, to overcome this problem ,we will use two solutions: Firstly  via using  principal component regression and second solution via using quantile regression. When mix between these methods together give as robust model against the  Multicollinearity problem. The simulation scenario and real data using in this study.

Downloads

Download data is not yet available.

References

Alhamzawi, R., “Tobit quantile regression with adaptive lasso penalty”, The 4th International Scientific Conference of Arab Statistics. 2013, 450 ISSN, pp. 1681-6870).

Cade, B. S. and Noon, B. R. “A gentle introduction to quantile regression . 2003.

Davino, C., Romano, R., & Vistocco, D. Handling multicollinearity in quantile regression through the use of principal component regression. METRON. 2022, 80(2), 153-174.

Eberly, L. E, Multiple linear regression. Topics in Biostatistics. 2007. 165-187.

Ergon, R., Granato, D., & Ares, G. Principal component regression (PCR) and partial least squares regression (PLSR). Mathematical and statistical methods in food science and technology Wiley Blackwell, Chichester. 2014. 121-42.

Kim, B., Kanai, M. I., Oh, Y., Kyung, M., Kim, E. K., Jang, I. H., ... & Lee, W. J. Response of the microbiome–gut–brain axis in Drosophila to amino acid deficit. Nature. 2021. 593(7860), 570-574.

Koenker, R. Quantile Regression. Cambridge Books. Cambridge University Press. 2005.

Koenker, R. and G. J. Bassett. Regression quantiles. Econometrica. 1978. 46, 33–50.

Koenker, R. and V. D’Orey. Algorithm AS 229: Computing regression quantiles. 1987.

Koenker, R., & Geling, O. Reappraising medfly longevity: a quantile regression survival analysis. Journal of the American Statistical Association. 2001. 96(454), 458-468.

Kostov, P., & Davidova, S. A quantile regression analysis of the effect of farmers’ attitudes and perceptions on market participation. Journal of Agricultural Economics. 2013. 64(1), 112-132.

Kyung, M. Bayesian analysis of quantile principal component regression model. The Korean Data & Information Science Society. 2021. 32(4), 739-755.

Sutter, J. M., Kalivas, J. H., & Lang, P. M. Which principal components to utilize for principal component regression. Journal of chemometrics. 1992. 6(4), 217-225.

Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological). 1996. 267-288.

Wang, H., & He, X. Detecting differential expressions in GeneChip microarray studies: a quantile approach. Journal of the American Statistical Association, 2007. 102(477), 104-112.

Downloads

Published

2023-12-30

How to Cite

H. Al-Sharoot, M., K. Mohammed, F., & N. Mayali, H. (2023). Lasso Quantile Principal Component Regression. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(4), Stat. 24–31. https://doi.org/10.29304/jqcsm.2023.15.41356

Issue

Vol. 15 No. 4 (2023): JQCM

Section

Statistic Articles

License

Copyright (c) 2024 Mohammed H. Al-Sharoot, Fatimah K. Mohammed, Hameedah N. Mayali

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Most read articles by the same author(s)