Optimizations for categorizations of explanatory variables in linear regression via generalized fused Lasso
Optimizations for categorizations of explanatory variables in linear regression via generalized fused Lasso
复制标题
通过广义融合套索优化线性回归中解释变量的分类
DOI：
10.1007/978-981-16-2765-1_38
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Yanagihara Hirokazu
中科院分区：
文献类型：
--
作者：
Ohishi Mineaki;Okamura Kensuke;Itoh Yoshimichi;Yanagihara Hirokazu
在线性回归中，可以通过将定量解释变量转换为分类变量来自然地考虑非线性结构。此外，较小的类别使估计更加灵活。然而，估计的灵活性和估计精度之间的权衡发生，因为参数的数量增加较小的分类。我们提出了一种估计方法，其中具有相同效果的类别的参数同样通过广义融合Lasso估计。通过这样的方法，可以预期模型的自由度降低，估计的灵活性和估计精度得以保持，并且解释变量的类别得以优化。我们将所提出的方法建模的公寓租金在东京的23个病房。
In linear regression, a non-linear structure can be naturally considered by transforming quantitative explanatory variables to categorical variables. Moreover, smaller categories make estimation more flexible. However, a trade-off between flexibility of estimation and estimation accuracy occurs because the number of parameters increases for smaller categorizations. We propose an estimation method wherein parameters for categories with equal effects are equally estimated via generalized fused Lasso. By such a method, it can be expected that the degrees of freedom for the model decreases, flexibility of estimation and estimation accuracy are maintained, and categories of explanatory variables are optimized. We apply the proposed method to modeling of apartment rents in Tokyo’s 23 wards.