Flexible Statistical Methods for Complex Structured Data Analysis

复杂的结构化数据来自许多领域，包括医学、工业和环境研究。因此，迫切需要开发强大的统计方法，以便能够更有效地利用信息，并使用更灵活的统计模型来分析数据。最具挑战性的统计问题之一是确定结果和风险因素之间的关系，并将结果模型用于结果预测。这方面的研究已经引起了广泛的关注，使用半参数或非参数方法的文献迅速增长，但灵活的方法来结合各种类型的风险影响仍然是非常需要的。

Complex structured data emerge from many fields including medical, industrial and environmental studies. Consequently, there is an urgent need for developing powerful statistical methods to enable more efficient use of information, and to analyze data using more flexible statistical models. One of the most challenging statistical issues is identification of the relationship between outcomes and risk factors, and ultilization of the resulting models for outcome prediction. Extensive research attention has been directed to this area and there has been a rapid growth in the literature using either semi-parametric or nonparametric approaches, but flexible approaches to incorporate various types of risk effects are still in great demand.