Variable selection in general multinomial logit models
Variable selection in general multinomial logit models
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DOI：
10.1016/j.csda.2014.09.009
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
G. Tutz;Wolfgang Pößnecker;L. Uhlmann
中科院分区：
文献类型：
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作者：
G. Tutz;Wolfgang Pößnecker;L. Uhlmann
多项logit模型的使用通常仅限于预测因子较少的应用，因为在高维环境中，最大似然估计往往会恶化。针对多项式模型的特殊结构，提出了一种稀疏诱导罚函数，该罚函数对与一个变量相关的参数进行分组惩罚。它的目的是处理一般的多项logit模型与全球预测和那些特定的响应类别的组合。一个近似梯度算法，有效地计算稳定的估计。自适应权重和改装程序，以提高变量的选择和预测性能。通过仿真研究和德国选民政党选择模型的应用，证明了所提出的方法的有效性。
The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate. A sparsity-inducing penalty is proposed that accounts for the special structure of multinomial models by penalizing the parameters that are linked to one variable in a grouped way. It is devised to handle general multinomial logit models with a combination of global predictors and those that are specific to the response categories. A proximal gradient algorithm is used that efficiently computes stable estimates. Adaptive weights and a refitting procedure are incorporated to improve variable selection and predictive performance. The effectiveness of the proposed method is demonstrated by simulation studies and an application to the modeling of party choice of voters in Germany.