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• W2103869087 abstract "The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the notion of transforming a given setup into one that is easier to work with, namely i.i.d. or Gaussian. As an application, the problem of nonparametric regression is addressed in detail; the model-free predictors are worked out, and shown to be applicable under minimal assumptions. Interestingly, model-free prediction in regression is a totally automatic technique that does not necessitate the search for an optimal data transformation before model fitting. The resulting model-free predictive distributions and intervals are compared to their corresponding model-based analogs, and the use of cross-validation is extensively discussed. As an aside, improved prediction intervals in linear regression are also obtained." @default.
• W2103869087 created "2016-06-24" @default.
• W2103869087 creator A5022975114 @default.
• W2103869087 date "2013-04-05" @default.
• W2103869087 modified "2023-09-27" @default.
• W2103869087 title "Model-free model-fitting and predictive distributions" @default.
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• W2103869087 doi "https://doi.org/10.1007/s11749-013-0317-7" @default.
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