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Additive Models for Quantile Regression: Model Selection and Confidence Bandaids

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We describe some recent development of nonparametric methods for estimating conditional quantile functions using additive models with total variation roughness penalties. We focus attention primarily on selection of smoothing parameters and on the construction of confidence bands for the nonparametric components. Both pointwise and uniform confidence bands are introduced; the uniform bands are based on the Hotelling (1939) tube approach. Some simulation evidence is presented to evaluate finite sample performance and the methods are also illustrated with an application to modeling childhood malnutrition in India. The methods described have been implemented in the R package {\tt quantreg}.

http://www.econ.uiuc.edu/roger/research/bandaids/bandaids.html

http://www.econ.uiuc.edu/roger/

This talk is part of the Statistics series.

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In person in the Max Perutz Lecture Theatre (CB2 0QH) and via Zoom, link:https://mrc-lmb-cam-ac-uk.zoom.us/j/91551032153?pwd=bUhESzgvZDlDTUllb1pCS1BQdFBlZz09 215 Hybrid: JDB Teaching Room, Engineering Department or Zoom: https://cam-ac-uk.zoom.us/meeting/register/tZ0tfuCvrDgjG9WNY52TL0qsu7PqO484Pad5

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