| 摘要: |
| 本文研究了Newton-Raphson 等算法无法进行时探寻更加稳定的数值解法的问题. 利用Böhning & Linday (1988) 提出的二次下界算法(Quadratic lower-bound), 文中在Logistic 回归模型下构造了极大似然函数的代理函数并进行数值模拟, 获得了二次下界算法是Newton-Raphson 算法的合理替代的结果, 推广了数值方法在Logistic 回归模型中的应用. |
| 关键词: minorization-maximization算法 Logistic回归模型 quadratic lower-bound算法 极大似然估计 Newton-Raphson算法 |
| DOI： |
| 分类号:O212.1 |
| 基金项目:国家自然科学基金(11101314). |
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| QUADRATIC LOWER-BOUND ALGORITHM FOR MAXIMUM LIKELIHOOD ESTIMATOR OF LOGISTIC REGRESSION ON PARAMETER AND ITS APPLICATION |
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WANG Jia, DING Jie-li
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School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
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| Abstract: |
| In this paper, we study how to explore more stable numerical solution when parameters cannot be solved by using Newton-Raphson algorithm. By using the quadratic lower bound algorithm that Böhning & Linday has proposed in 1988, we construct a surrogate function for maximum likelihood function under Logistic regression model and the simulation results verify that quadratic lower bound algorithm is a reasonable algorithm of Newton-Raphson algorithm, which extend numerical method's application under Logistic regression model. |
| Key words: minorization-maximization algorithm Logistic regression model quadratic lower-bound algorithm maximum likelihood estimator Newton-Raphson algorithm |
