Adjusted Adaptive LASSO in High-dimensional Poisson Regression Model

Algamal, Zakariya Yahya; Lee, Muhammad Hisyam

doi:10.5539/mas.v9n4p170

Cited by 16 publications

(5 citation statements)

References 25 publications

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“…In this section, "the same simulation settings of [29] and [4] For all the simulation examples 1-3, the response variable is generated according to PRM as y i ∼ P o(exp(x i T β)). Simulations 4-6 are the same as the setting of simulations 1 C 3 where the response variable is generated according to NBRM with conditional mean exp(x i T β) and τ = 2.…”

Section: Simulation Resultsmentioning

confidence: 99%

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Algamal

2019

Stat., optim. inf. comput.

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Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Count data regression modeling has received much attention in several science fields in which the Poisson and negative binomial regression models are the most basic models. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, firefly algorithm is proposed to perform variable selection for count data regression models. Extensive simulation studies and two real data applications are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Algamal

2019

Stat., optim. inf. comput.

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“…In order to reduce the parameter to a very low variance, this term is added. The operation of Lasso regression is quite similar to ridge regression and helps to penalize the absolute size of the regression co-efficients [37]. In order to improve the accuracy and to mitigate the variability, the model is used and is expressed as argmin…”

Section: Modified Adaboostrt Algorithm Based On Ridge Lasso and Soft Thresholdingmentioning

confidence: 99%

Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

Prabhakar

Rajaguru²

2020

Heliyon

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The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recording the EEG signals, the activities of the neurons are recorded from various scalp regions which has varied characteristics and has a very low magnitude. Therefore, human interpretation of such signals is very difficult and consumes a lot of time. Hence, with the advent of Computer Aided Diagnosis (CAD) Techniques, identifying the normal versus alcoholic EEG signals has been of great utility in the medical field. In this work, we perform the initial clustering of the alcoholic EEG signals by means of using Correlation Dimension (CD) for easy feature extraction and then the suitable features are selected in it by means of employing various distance metrics like correlation distance, city block distance, cosine distance and chebyshev distance. Proceeding in such a methodology aids and assures that a good discrimination could be achieved between normal and alcoholic EEG signals using non-linear features. Finally, classification is then carried out with the suitable classifiers chosen such as Adaboost.RT classifier, the proposed Modified Adaboost.RT classifier by means of introducing Ridge and Lasso based soft thresholding technique, Random Forest with bootstrap resampling technique, Artificial Neural Networks (ANN) such as Radial Basis Functions (RBF) and Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) with Linear, Polynomial and RBF Kernel, Naïve Bayesian Classifier (NBC), K-means classifier, and K Nearest Neighbor (KNN) Classifier and the results are analyzed. Results report a comparatively high classification accuracy of about 98.99% when correlation distance metrics are utilized with CD and the proposed Modified Adaboost.RT classifier using Ridge based soft thresholding technique.

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“…Other penalized estimators, such as the LASSO estimator (Hossain & Ahmed, 2012), adaptive LASSO estimator (Algamal & Lee, 2015;Hossain & Ahmed, 2012;Ivanoff, Picard, & Rivoirard, 2016), elastic net estimator (Noori Asl, Bevrani, & Arabi Belaghi, 2022), and bridge estimator (Chowdhury, Chatterjee, Mallick, Banerjee, & Garai, 2019), have been proposed, but they also use zero as the shrinkage center and mainly focus on variable selection rather than limiting multicollinearity. The Jackknifed version and modified Jackknifed version of PRE, PLE, and PKLE (Oranye & Ugwuowo, 2022;Rasheed, Sadik, & Algamal, 2022;Turkan & Ozel, 2016) were also proposed to obtain an almost unbiased estimator by applying the Jackknifed procedure of bias reduction.…”

Section: Introductionmentioning

confidence: 99%

Poisson average maximum likelihood‐centered penalized estimator: A new estimator to better address multicollinearity in Poisson regression

Sheng

Wang

Yao

et al. 2023

Statistica Neerlandica

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The Poisson ridge estimator (PRE) is a commonly used parameter estimation method to address multicollinearity in Poisson regression (PR). However, PRE shrinks the parameters toward zero, contradicting the real association. In such cases, PRE tends to become an insufficient solution for multicollinearity. In this work, we proposed a new estimator called the Poisson average maximum likelihood‐centered penalized estimator (PAMLPE), which shrinks the parameters toward the weighted average of the maximum likelihood estimators. We conducted a simulation study and case study to compare PAMLPE with existing estimators in terms of mean squared error (MSE) and predictive mean squared error (PMSE). These results suggest that PAMLPE can obtain smaller MSE and PMSE (i.e., more accurate estimates) than the Poisson ridge estimator, Poisson Liu estimator, and Poisson K‐L estimator when the true s have the same sign and small variation. Therefore, we recommend using PAMLPE to address multicollinearity in PR when the signs of the true s are known to be identical in advance.

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Adjusted Adaptive LASSO in High-dimensional Poisson Regression Model

Cited by 16 publications

References 25 publications

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

Poisson average maximum likelihood‐centered penalized estimator: A new estimator to better address multicollinearity in Poisson regression

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