Regression is one of the most fundamental statistical inference problems. A broad definition of regression problems is as estimation of the distribution of an outcome using a family of probability models indexed by covariates. Despite the ubiquitous nature of regression problems and the abundance of related methods and results there is a surprising gap in the literature. There are no well established methods for regression with a varying dimension covariate vectors, despite the common occurrence of such problems. In this paper we review some recent related papers proposing varying dimension regression by way of random partitions.

翻译：回归是最基本的统计推断问题之一。回归问题的广义定义是：利用由协变量索引的概率模型族来估计结果的分布。尽管回归问题无处不在，且相关方法与研究成果丰硕，但文献中仍存在一个令人惊讶的空白。目前尚无成熟的方法处理协变量向量维度变化的回归问题，尽管这类问题在实际中颇为常见。本文综述了近期通过随机划分实现变维回归的相关研究论文。