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  "Title": "Robust Marginal Integration Procedures",
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  "Authors@R": "c(person(\"Alejandra\", \"Martinez\", role=c(\"aut\", \"cre\"), email=\"ale_m_martinez@hotmail.com\"),\nperson(\"Matias\", \"Salibian-Barrera\", role = c(\"aut\"), email = \"matias@stat.ubc.ca\"))",
  "Description": "Three robust marginal integration procedures for additive\nmodels based on local polynomial kernel smoothers. As a\npreliminary estimator of the multivariate function for the\nmarginal integration procedure, a first approach uses local\nconstant M-estimators, a second one uses local polynomials of\norder 1 over all the components of covariates, and the third\none uses M-estimators based on local polynomials but only in\nthe direction of interest. For this last approach, estimators\nof the derivatives of the additive functions can be obtained.\nAll three procedures can compute predictions for points outside\nthe training set if desired. See Boente and Martinez (2017)\n<doi:10.1007/s11749-016-0508-0> for details.",
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        "rmargint"
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