On linearized ridge logistic estimator in the presence of multicollinearity

Jadhav, N. H.

doi:10.1007/s00180-019-00935-6

Cited by 9 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where  is the true parameter vector which is restricted in many simulation studies to be chosen as the normalized eigenvector corresponding to the largest eigenvalue of X X  so that k ) defined in Eq. [8], and its corresponding scalar parameters ( 1  , 2  , and 3  ) respectively, defined in [24] for the proposed estimator (MRLSVDE).…”

Section: Simulation Studymentioning

confidence: 99%

Modified Ridge Logistic Estimator Based on Singular Value Decomposition

Kamal,

Hussein

2023

The Egyptian Statistical Journal

View full text Add to dashboard Cite

Section: Simulation Studymentioning

confidence: 99%

Modified Ridge Logistic Estimator Based on Singular Value Decomposition

Kamal,

Hussein

2023

The Egyptian Statistical Journal

View full text Add to dashboard Cite

“…In addition, there are many studies that focused on the ridge logistic estimator (RLE), such as; Kibria et al (2012) evaluated some biasing ridge parameters (k), Nja et al (2013) introduced the modified logistic ridge regression estimator (MLRE), Wu and Asar (2016) suggested the almost unbiased ridge logistic estimator (AURLE), Asar and Genc (2017) proposed the two-parameter ridge estimator in logistic regression. Varathan and Wijekoon (2017) introduced an optimal generalized logistic estimator based on quasi-likelihood (QL) estimation, Jadhav (2020) proposed the linearized ridge logistic estimator (LRLE), Lukman et al (2020) introduced the modified ridge type logistic estimator, Varathan (2022) proposed a modified almost unbiased ridge logistic estimator. Abonazel et al (2023) proposed the probit modified ridge and probit Dawoud −Kibria estimators for the probit regression model.…”

Section: Introductionmentioning

confidence: 99%

A New Estimator to Combat Multicollinearity in Logistic Regression Model

Hussein,

Abd El-Rahman

2024

المجلة العلمية للدراسات والبحوث المالية والإدارية‎

View full text Add to dashboard Cite

This paper proposes a new estimator based on the singular value decomposition technique of the design matrix to remedy multicollinearity in the binary logistic model. The proposed estimator is called the SVD-based maximum likelihood logistic estimator. The theoretical properties of this estimator and its superiority over some existing estimators is derived in the sense of the matrix mean squared error criterion. The choice of scalar parameter for this estimator is discussed. A Monte Carlo simulation study has been conducted to compare the performance of the proposed estimator with the existing maximum likelihood estimator and ridge logistic estimator in terms of the mean squared error criterion. Moreover, a real data application is presented to illustrate the potential benefits of the proposed estimator and satisfy the theoretical findings. The results from the simulation study and the empirical application reveal that the proposed estimator works well and outperforms existing estimators in scalar mean squared error sense.

show abstract

“…In this paper, we use ridge estimation algorithm to estimate parameters of the permanent and induced magnetic fields. Compared with the least square method, ridge estimation algorithm can overcome the estimation deviation caused by the insufficient attitude of the vehicles [7][8]. This paper consists of four parts as follows: Firstly, the interference compensation model and ridge estimation algorithm are introduced.…”

Section: Introductionmentioning

confidence: 99%

Compensation of UUV magnetic field interference based on ridge estimation

Zhang

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The ocean magnetic anomaly detection based on unmanned underwater vehicles has great significance in military field, such as anti-submarine, anti-mine, and ship magnetic property evaluation. But it is easy for magnetometer fixed on vehicles to be affected by the magnetic interference of vehicles ferromagnetic equipment and electronic equipment. The magnetic interferential field seriously affects the measurement accuracy of the magnetometer. In this paper, ridge estimation method is proposed to estimate magnetic field interference coefficient and compensate magnetic field. A simulation is conducted to verify the validity of the method. And an experiment is designed and implemented on a vehicle. This method can reduce the measurement error of the magnetometer from 11000.0nT to 155.6nT and compensate 98.55% of the magnetic field interference. Both of simulation and experimental results show that ridge estimation method can effectively overcome the influence of complex collinearity of data and better compensate the interference magnetic field.

show abstract

On linearized ridge logistic estimator in the presence of multicollinearity

Cited by 9 publications

References 11 publications

Modified Ridge Logistic Estimator Based on Singular Value Decomposition

Modified Ridge Logistic Estimator Based on Singular Value Decomposition

A New Estimator to Combat Multicollinearity in Logistic Regression Model

Compensation of UUV magnetic field interference based on ridge estimation

Contact Info

Product

Resources

About