Using LASSO to Assist Imputation and Predict Child Well-being
Using LASSO to Assist Imputation and Predict Child Well-being
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使用 LASSO 辅助插补并预测儿童福祉
DOI：
10.1177/2378023118814623
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发表时间：
2019
期刊：
影响因子：
4.5
通讯作者：
S. Yamauchi
中科院分区：
文献类型：
--
作者：
Diana M. Stanescu;Erik H. Wang;S. Yamauchi
本文记录了一种预测儿童福祉的方法，该方法使用了来自脆弱家庭和儿童福祉研究的数据，这些数据代表了美国大城市的出生情况。作者使用最小绝对收缩和选择算子（LASSO）对数据进行预处理。然后他们应用阿米莉亚算法来计算缺失的数据。最后，他们再次使用LASSO对输入数据进行预测。作者报告了该方法对六个结果变量的性能。该方法在可变材料硬度条件下达到了最佳性能。作者预测的样本外均方误差为0.019，在脆弱家庭挑战赛的所有参赛作品中最低。作者发现，在具有高预测能力的变量中，来自母亲调查的变量占主导地位。此外，过去物质困难的组成部分强有力地预测了当前的物质困难。
This article documents an approach to predicting children’s well-being using data from the Fragile Families and Child Wellbeing Study, which are representative of births in large U.S. cities. The authors use the least absolute shrinkage and selection operator (LASSO) to preprocess the data. They then apply the Amelia algorithm to impute missing data. Finally, they use LASSO again for prediction with the imputed data. The authors report the performance of this approach for six outcome variables. The approach achieves the best performance for the variable material hardship. The out-of-sample mean squared error of the authors’ prediction is 0.019, the lowest among all submissions in the Fragile Families Challenge. The authors find that among variables with high predictive power, variables from mother surveys dominate. Furthermore, components of material hardship in the past strongly predict current material hardship.
影响因子：
5.8
作者：
Simon N;Friedman J;Hastie T;Tibshirani R
通讯作者：
Tibshirani R