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• W1946367946 abstract "Summary Motivated by the conditional growth charts problem, we develop a method for conditional quantile analysis when predictors take values in a functional space. The method proposed aims at estimating conditional distribution functions under a generalized functional regression framework. This approach facilitates balancing of model flexibility and the curse of dimensionality for the infinite dimensional functional predictors. Its good performance in comparison with other methods, both for sparsely and for densely observed functional covariates, is demonstrated through theory as well as in simulations and an application to growth curves, where the method proposed can, for example, be used to assess the entire growth pattern of a child by relating it to the predicted quantiles of adult height." @default.
• W1946367946 created "2016-06-24" @default.
• W1946367946 creator A5050634491 @default.
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• W1946367946 date "2011-10-10" @default.
• W1946367946 modified "2023-10-16" @default.
• W1946367946 title "Conditional Quantile Analysis When Covariates are Functions, with Application to Growth Data" @default.
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• W1946367946 doi "https://doi.org/10.1111/j.1467-9868.2011.01008.x" @default.
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