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POISSON RIDGE REGRESSION ESTIMATORS

Jerson Mohamad et al.

Advances and Applications in Statistics2026https://doi.org/10.17654/0972361726007article
ABDC C
Weight
0.50

What the paper says

This paper proposes a new Poisson ridge regression estimator using grid search. The new and known ridge estimators were then compared based on MSE criterion using Monte Carlo simulation. Different values of parameters were considered, such as sample size of greater than or equal to 10; correlation values of 0.85 to 0.99; and number of explanatory variables of greater than or equal to 2. Results showed that the proposed estimator outperformed the known estimators in most cases considered.

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https://doi.org/https://doi.org/10.17654/0972361726007

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@article{jerson2026,
  title        = {{POISSON RIDGE REGRESSION ESTIMATORS}},
  author       = {Jerson Mohamad et al.},
  journal      = {Advances and Applications in Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.17654/0972361726007},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.