广义线性和可加模型有序预测因子的统计推断及其在支气管肺发育不良中的应用
Statistical Inference for Ordinal Predictors in Generalized Linear and Additive Models with Application to Bronchopulmonary Dysplasia
摘要：
离散但有序的协变量在应用统计学中非常常见，并且已经提出了一些正则化拟合程序来正确处理统计建模中的有序预测变量。在这项研究中，我们将展示如何二次处罚相邻的哑系数的有序预测在文献中提出的广义可加模型的框架中，使统计推断工具，以及有序预测。出于新生儿医学的应用程序，我们讨论是否得到的结果时，构建置信区间和检验显着性的光滑项在广义可加模型是有用的有序预测/处罚。
英文摘要：
Discrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in statistical modeling. In this study, we show how quadratic penalties on adjacent dummy coefficients of ordinal predictors proposed in the literature can be incorporated in the framework of generalized additive models, making tools for statistical inference developed there available for ordinal predictors as well. Motivated by an application from neonatal medicine, we discuss whether results obtained when constructing confidence intervals and testing significance of smooth terms in generalized additive models are useful with ordinal predictors/penalties as well.
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