A penalty function in binary logistic regression
Issued Date
2018
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2561
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Edition
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eng
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application/pdf
No. of Pages/File Size
186 leaves
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b207809
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ผลงานนี้เผยแพร่ภายใต้ สัญญาอนุญาตครีเอทีฟคอมมอนส์แบบ แสดงที่มา-ไม่ใช้เพื่อการค้า-ไม่ดัดแปลง 4.0 (CC BY-NC-ND 4.0)
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National Institute of Development Administration. Library and Information Center
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Citation
Piyada Phrueksawatnon (2018). A penalty function in binary logistic regression. Retrieved from: https://repository.nida.ac.th/handle/662723737/6422.
Title
A penalty function in binary logistic regression
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Abstract
An algorithm is proposed to determine the logistic ridge parameter minimizing
the MSE of the estimated parameter estimators, together with a theorem on the upperbound of the optimal logistic ridge parameter to facilitate the nonlinear optimization.
A simulation is used to evaluate the relative efficiencies of the proposed estimator and
other six well-known ridge estimators with respect to the maximum likelihood
estimator. The simulation results confirm that the relative efficiency of the proposed
estimator is highest among other well-known estimators. Finally, a real-life data set is
used to repeat the evaluation and the conclusion is the same as in the simulation
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Thesis (Ph.D. (Statistics))--National Institute of Development Administration, 2018