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Regression with Variable Dimension Covariates

Author

Listed:
  • Peter Mueller

    (University of Texas at Austin)

  • Fernando Andrés Quintana

    (Pontificia Universidad Católica de Chile)

  • Garritt L. Page

    (Brigham Young University)

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.

Suggested Citation

  • Peter Mueller & Fernando Andrés Quintana & Garritt L. Page, 2024. "Regression with Variable Dimension Covariates," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 185-198, November.
    • Handle: RePEc:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-023-00329-3
    DOI: 10.1007/s13171-023-00329-3
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    More about this item

    Keywords

    Density regression; Clustering; Partition; Missing data;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health

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