Global and local distance-based generalized linear models
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DOI: 10.1007/s11749-015-0447-1
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Cited by:
- Beibei Yuan & Willem Heiser & Mark Rooij, 2019. "The δ-Machine: Classification Based on Distances Towards Prototypes," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 442-470, October.
- Amparo Baíllo & Aurea Grané, 2021. "Subsampling and Aggregation: A Solution to the Scalability Problem in Distance-Based Prediction for Mixed-Type Data," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
- S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.
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More about this item
Keywords
Distance-based prediction; Functional data analysis ; Generalized linear model; Iteratively weighted least squares; Local likelihood; R package dbstats ; 62G08; 62J12;All these keywords.
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