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arXiv · 2309.14120

Regression with Variable Dimension Covariates

Peter Mueller·Fernando Andrés Quintana·Garritt L. Page
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Abstract

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.

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Peter Mueller, Fernando Andrés Quintana, Garritt L. Page. 2023-09-25. Regression with Variable Dimension Covariates. https://arxiv.org/abs/2309.14120

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