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Sufficient Reductions in Regressions With Exponential Family Inverse Predictors

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  • Efstathia Bura
  • Sabrina Duarte
  • Liliana Forzani

Abstract

We develop methodology for identifying and estimating sufficient reductions in regressions with predictors that, given the response, follow a multivariate exponential family distribution. This setup includes regressions where predictors are all continuous, all categorical, or mixtures of categorical and continuous. We derive the minimal sufficient reduction of the predictors and its maximum likelihood estimator by modeling the conditional distribution of the predictors given the response. Whereas nearly all extant estimators of sufficient reductions are linear and only partly capture the sufficient reduction, our method is not limited to linear reductions. It also provides the exact form of the sufficient reduction, which is exhaustive, its maximum likelihood (ML) estimates via an iterated reweighted least-square (IRLS) estimation algorithm, and asymptotic tests for the dimension of the regression. Supplementary materials for this article are available online.

Suggested Citation

  • Efstathia Bura & Sabrina Duarte & Liliana Forzani, 2016. "Sufficient Reductions in Regressions With Exponential Family Inverse Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1313-1329, July.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:515:p:1313-1329
    DOI: 10.1080/01621459.2015.1093944
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    Citations

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    Cited by:

    1. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    2. Li, Junlan & Wang, Tao, 2021. "Dimension reduction in binary response regression: A joint modeling approach," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    3. Forzani, Liliana & Rodriguez, Daniela & Smucler, Ezequiel & Sued, Mariela, 2019. "Sufficient dimension reduction and prediction in regression: Asymptotic results," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 339-349.
    4. Barbarino, Alessandro & Bura, Efstathia, 2024. "Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 1-18.
    5. Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.
    6. Kapla, Daniel & Fertl, Lukas & Bura, Efstathia, 2022. "Fusing sufficient dimension reduction with neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    7. Szretter Noste, María Eugenia, 2019. "Using DAGs to identify the sufficient dimension reduction in the Principal Fitted Components model," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 317-320.
    8. Forzani, Liliana & García Arancibia, Rodrigo & Llop, Pamela & Tomassi, Diego, 2018. "Supervised dimension reduction for ordinal predictors," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 136-155.
    9. Alessandro Barbarino & Efstathia Bura, 2017. "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series 2017-004, Board of Governors of the Federal Reserve System (U.S.).
    10. Koplin, Eric & Forzani, Liliana & Tomassi, Diego & Pfeiffer, Ruth M., 2024. "Sufficient dimension reduction for a novel class of zero-inflated graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).

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