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，迫切需要发展强大的统计方法，以便更有效地利用信息，并使用更灵活的统计模型分析数据。最具挑战性的统计问题之一是确定结果与风险因素之间的关系，并利用由此产生的模型进行结果预测。广泛的研究关注已经指向这一领域，并且使用半参数或非参数方法的文献已经迅速增长，但是仍然需要灵活的方法来纳入各种类型的风险效应。

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.