LASSO指标范围及应用的参考文献-Go服务器开发

LASSO（Least Absolute Shrinkage and Selection Operator）是一种特征选择和正则化的方法，它通过对模型系数进行约束来提高模型的泛化能力。LASSO指标范围包括了回归分析、分类分析、生存分析等领域，被广泛应用于数据挖掘、统计建模和机器学习等领域。

以下是关于LASSO应用和参考文献：

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1-22.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.
Chen, S., Billings, S.A., & Luo,W.(2018). Sparse identification of nonlinear systems using Lasso-type regularisation. International Journal of Control,91(6),1287-1297.
Liang,Y., Wang,S.Y., Zhou,J.Y.(2019). A double LASSO method to identify a sparse subset of interactions in high-dimensional genomic data analysis.Computational and Mathematical Methods in Medicine,Article ID: 3737595.
Shen,X.Y.(2013). The application of Lasso regression in prediction analysis with clinical data.Journal of Biomedical Research and Clinical Practice,3(2):35-38.
Wang,K.X.(2020). Application of LASSO logistic regression model in predicting colorectal cancer recurrence. Journal of Medical Systems,44(10),1-9.
Xu, H., & Liu, T. (2016). A new approach for variable selection based on LASSO method and genetic algorithm. Computational Intelligence and Neuroscience, 2016, 1-10.
Zhang,T., & Yu,B.(2005). Boosting with early stopping: convergence and consistency.The Annals of Statistics,33(4),1530-1548.
Zou,H.,& Li,R.(2008). One-step sparse estimates in nonconcave penalized likelihood models.Annals of Statistics,36(4),1509-1533.