Regression with Variable Dimension Covariates

Peter Mueller (), Fernando Andrés Quintana () and Garritt L. Page ()
Additional contact information
Peter Mueller: University of Texas at Austin
Fernando Andrés Quintana: Pontificia Universidad Católica de Chile
Garritt L. Page: Brigham Young University

Sankhya A: The Indian Journal of Statistics, 2024, vol. 86, issue 1, No 11, 185-198

Abstract: 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.

Keywords: Density regression; Clustering; Partition; Missing data (search for similar items in EconPapers)
JEL-codes: C11 H51 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13171-023-00329-3

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