Robust biased estimators for Poisson regression model: Simulation and applications

Lukman, Adewale F.; Arashi, Mohammad; Prokaj, Vilmos

doi:10.1002/cpe.7594

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…Future research could explore applying our estimator to other regression models, such as the zero-inflated negative binomial regression model, the zero-inflated Poisson regression model, and CMPRM. Extending our work to provide a robust biased estimation of PIGRM, following approaches by Lukman et al [32], Omara [36], and Lukman et al [32], could be beneficial.…”

Section: Discussionmentioning

confidence: 98%

Liu-Type Estimator for the Poisson-Inverse Gaussian Regression Model: Simulation and Practical Applications

Alrweili

2024

Stat., optim. inf. comput.

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The Poisson-Inverse Gaussian regression model (PIGRM) is commonly used to analyze count datasets with over-dispersion. While the maximum likelihood estimator (MLE) is a standard choice for estimating PIGRM parameters, its performance may be suboptimal in the presence of correlated explanatory variables. To overcome this limitation, we introduce a novel Liu-type estimator for PIGRM. Our analysis includes an examination of the matrix mean square error (MMSE) and scalar mean square error (SMSE) properties of the proposed estimator, comparing them with those of the MLE, ridge, and Liu estimators. We also present several parameters of the Liu-type estimator for PIGRM. We evaluated the performance of the proposed estimator through a simulation study and application to real-life data, using SMSE as the primary evaluation criterion. Our results demonstrate that the proposed estimators outperform the MLE, ridge, and Liu estimators in both simulated and real-world scenarios.

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Section: Discussionmentioning

confidence: 98%

Liu-Type Estimator for the Poisson-Inverse Gaussian Regression Model: Simulation and Practical Applications

Alrweili

2024

Stat., optim. inf. comput.

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“…This approach allowed us to assess the effectiveness of our proposed method under different scenarios and make reliable conclusions about its performance. As per Alao et al [48] and Lukman et al [49][50][51][52][53] approach, the model was deliberately contaminated with outliers using the following equation:…”

Section: Simulation Studies and Discussionmentioning

confidence: 99%

Robust hybrid algorithms for regularization and variable selection in QSAR studies

Nwaeme,

Lukman

2023

J. Nig. Soc. Phys. Sci.

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This study introduces a robust hybrid sparse learning approach for regularization and variable selection. This approach comprises two distinct steps. In the initial step, we segment the original dataset into separate training and test sets and standardize the training data using its mean and standard deviation. We then employ either the LASSO or sparse LTS algorithm to analyze the training set, facilitating the selection of variables with non-zero coefficients as essential features for the new dataset. Secondly, the new dataset is divided into training and test sets. The training set is further divided into k folds and evaluated using a combination of Random Forest, Ridge, Lasso, and Support Vector Regression machine learning algorithms. We introduce novel hybrid methods and juxtapose their performance against existing techniques. To validate the efficacy of our proposed methods, we conduct a comprehensive simulation study and apply them to a real-life QSAR analysis. The findings unequivocally demonstrate the superior performance of our proposed estimator, with particular distinction accorded to SLTS+LASSO. In summary, the twostep robust hybrid sparse learning approach offers an effective regularization and variable selection applicable to a wide spectrum of real-world problems.

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“…We analyzed the Longley data to predict the total derived employment, which is a linear function of the following predictors: gross national product implicit price deflator, gross national product, unemployment, size of armed forces, and non-institutional population 14 years of age and over 33 , 38 – 40 , 42 , 43 . The literature indicates that the model suffers from multicollinearity.…”

Section: Real-life Applicationmentioning

confidence: 99%

Robust-stein estimator for overcoming outliers and multicollinearity

Lukman

Farghali

Kibria

et al. 2023

Sci Rep

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Linear regression models with correlated regressors can negatively impact the performance of ordinary least squares estimators. The Stein and ridge estimators have been proposed as alternative techniques to improve estimation accuracy. However, both methods are non-robust to outliers. In previous studies, the M-estimator has been used in combination with the ridge estimator to address both correlated regressors and outliers. In this paper, we introduce the robust Stein estimator to address both issues simultaneously. Our simulation and application results demonstrate that the proposed technique performs favorably compared to existing methods.

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Robust biased estimators for Poisson regression model: Simulation and applications

Cited by 11 publications

References 46 publications

Liu-Type Estimator for the Poisson-Inverse Gaussian Regression Model: Simulation and Practical Applications

Liu-Type Estimator for the Poisson-Inverse Gaussian Regression Model: Simulation and Practical Applications

Robust hybrid algorithms for regularization and variable selection in QSAR studies

Robust-stein estimator for overcoming outliers and multicollinearity

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