Regression Modeling Strategies

书籍介绍(英文)

There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.


有很多书是有关个人stastical工具方面的知识的最佳来源（生存模式，一般线性模型等），但数据分析如何选择和使用多种工具的艺术。在查特菲尔德的话“...学生一般知道，例如regressin的技术细节，但未必是何时以及如何运用它。这就说明了在文学之间的技术和解决问题的统计教学需要更好的平衡战略。“无论是分析风险因素，在观察研究或发展预测模型的偏差调整，有一些共同的问题，很少有回归文本的地址。例如，有失踪的大多数数据集之一，是可能会遇到（在教科书以外使用的！），但大多数回归文本不包括这些数据的有效处理方法，以及缺少数据的文本不包括回归建模。