Journal
JOURNAL OF MULTIVARIATE ANALYSIS
Volume 171, Issue -, Pages 339-349Publisher
ELSEVIER INC
DOI: 10.1016/j.jmva.2018.12.003
Keywords
Exponential family; Generalized linear model; Inverse regression; Maximum likelihood; Sufficient dimension reduction
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Funding
- CONICET fellowship
- Abdus Salam International Center for Theoretical Physics (ICTP)
- Universidad de Buenos Aires [20020170100330BA]
- ANPYCT, Argentina [PICT-201-0377]
- Universidad Nacional del Litoral [500-040, 501-499, 500-062]
- CONICET [PIP 742]
- ANPCYT Argentina [PICT 2012-2590]
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We consider model-based sufficient dimension reduction for generalized linear models and prove the consistency and asymptotic normality of the prediction estimator studied empirically for the normal case by Adragni and Cook (2009) when a sample version of the sufficient dimension reduction is used. Moreover, we provide a formula for the prediction that does need require explicitly computing the reduction. (C) 2018 Elsevier Inc. All rights reserved.
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