Wu Yichao, Liu Yufeng
Department of Statistics, North Carolina State University, Raleigh, NC 27695 (
J Comput Graph Stat. 2013 Apr 1;22(2):379-395. doi: 10.1080/10618600.2012.680823.
Functional and longitudinal data are becoming more and more common in practice. This paper focuses on sparse and irregular longitudinal data with a multicategory response. The predictor consists of sparse and irregular observations, potentially contaminated with measurement errors, on the predictor trajectory. To deal with this type of complicated predictors, we borrow the strength of large margin classifiers in statistical learning for classification of sparse and irregular longitudinal data. In particular, we propose functional robust truncated-hinge-loss support vector machines to perform multicategory classification with the aid of functional principal component analysis.
在实际应用中，功能数据和纵向数据越来越普遍。本文聚焦于具有多类别响应的稀疏且不规则的纵向数据。预测变量由预测变量轨迹上稀疏且不规则的观测值组成，这些观测值可能受到测量误差的影响。为了处理这类复杂的预测变量，我们借鉴统计学习中大型边际分类器的优势来对稀疏且不规则的纵向数据进行分类。具体而言，我们提出功能稳健截断铰链损失支持向量机，借助功能主成分分析来进行多类别分类。
