Statistical classification and curve estimation in some nonstandard cases

我的研究计划的主要部分是在缺少数据的情况下进行统计分类。统计分类长期以来一直被用作几乎所有物理、生物和社会科学领域的主要决策程序。它是基于实体的其他特征来预测与实体相关的兴趣特征。兴趣的特征通常被称为类隶属度，它总是一个绝对的/离散的量，如健康或疾病、正确或不正确、低或中或高、0或1等。特征集通常是数值的有限集合。一个简单的例子是，根据心跳、血压、年龄等测量数据，将一个人归类为患有某种疾病。接下来的问题是找到一个分类器（一个函数），它可以根据相应的特征集尽可能地预测一个人的类隶属度；这里的“最佳”是指具有尽可能低的误分类错误率。

The main part of my research proposal involves statistical classification in the presence of missing data. Statistical classification has long been used as a major decision making procedure in almost all areas of physical, biological, and social sciences. It is about predicting a characteristic of interest associated with an entity based on the entity's other features. The characteristic of interest, which is usually called the class membership, is always a categorical/discrete quantity such as healthy or ill, correct or incorrect, low or medium or high, 0 or 1, etc. The feature set is typically a finite collection of numerical values. A simple example is the case where a person is to be classified as having a certain type of illness, based on measurements such as heart-beats, blood pressure, age, etc. The problem is then to find a classifier (a function) that can predict the class membership of a person, based on its corresponding feature set, as best as possible; here "best" means having the lowest possible misclassification error rate.