REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA

Ali Hameed; Zainab Kazem Mezher; Najlaa Ali Dhumad

REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA

Abstract

Logistic regression models play an important role in analyzing binary classification problems in medical and biological data. One common method for estimating the parameters of a logistic regression model is the maximum likelihood method. However, this method does not perform well in high-dimensional settings or in the presence of multicollinearity. To overcome these problems, a penalty term is added to the objective function. In this paper, we propose a method for parameter estimation and variable selection in logistic regression models using an $L_{0\ }$ -like arctangent (Atan) regularization approach. The Atan penalty, which is based on the arctangent function, enjoys oracle properties. The performance of the regularized logistic regression model with the Atan penalty is compared with that of the fused lasso and the SELO penalty. Monte Carlo simulation studies are conducted under different sample sizes and different standard deviation settings. In addition, a real data set is used to evaluate the performance of the proposed method. The results show that the proposed estimator outperforms the competing methods (fused lasso and SELO) in terms of both estimation accuracy and variable selection.

Keywords

References

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Details

Primary Language

English

Subjects

Statistics (Other)

Journal Section

Research Article

Authors

Ali Hameed ^*
0000-0003-3604-2491
Iraq

Zainab Kazem Mezher This is me
0009-0006-5487-1057
Iraq

Najlaa Ali Dhumad This is me
0009-0007-3166-3172
Iraq

Publication Date

August 7, 2026

Submission Date

October 5, 2025

Acceptance Date

January 12, 2026

Published in Issue

Year 2026 Volume: 16 Number: 8

IZ

https://izlik.org/JA33EJ96RX

Cite

RIS / Bibtex

APA

Hameed, A., Kazem Mezher, Z., & Ali Dhumad, N. (2026). REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA. TWMS Journal of Applied and Engineering Mathematics, 16(8), 1035-1041. https://izlik.org/JA33EJ96RX

AMA

1.Hameed A, Kazem Mezher Z, Ali Dhumad N. REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA. JAEM. 2026;16(8):1035-1041. https://izlik.org/JA33EJ96RX

Chicago

Hameed, Ali, Zainab Kazem Mezher, and Najlaa Ali Dhumad. 2026. “REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA”. TWMS Journal of Applied and Engineering Mathematics 16 (8): 1035-41. https://izlik.org/JA33EJ96RX.

EndNote

Hameed A, Kazem Mezher Z, Ali Dhumad N (August 1, 2026) REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA. TWMS Journal of Applied and Engineering Mathematics 16 8 1035–1041.

IEEE

[1]A. Hameed, Z. Kazem Mezher, and N. Ali Dhumad, “REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA”, JAEM, vol. 16, no. 8, pp. 1035–1041, Aug. 2026, [Online]. Available: https://izlik.org/JA33EJ96RX

ISNAD

Hameed, Ali - Kazem Mezher, Zainab - Ali Dhumad, Najlaa. “REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA”. TWMS Journal of Applied and Engineering Mathematics 16/8 (August 1, 2026): 1035-1041. https://izlik.org/JA33EJ96RX.

JAMA

1.Hameed A, Kazem Mezher Z, Ali Dhumad N. REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA. JAEM. 2026;16:1035–1041.

MLA

Hameed, Ali, et al. “REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA”. TWMS Journal of Applied and Engineering Mathematics, vol. 16, no. 8, Aug. 2026, pp. 1035-41, https://izlik.org/JA33EJ96RX.

Vancouver

1.Ali Hameed, Zainab Kazem Mezher, Najlaa Ali Dhumad. REGULARIZED LOGISTIC REGRESSION WITH THE ATAN IN HIGH DIMENSIONAL DATA. JAEM [Internet]. 2026 Aug. 1;16(8):1035-41. Available from: https://izlik.org/JA33EJ96RX