Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105242
Title: Nonparametric regression with discrete covariate and missing values
Authors: Chen, S.X.
Tang, C.Y. 
Keywords: Discrete kernel smoothing
Imputation
Missing values
Nonparametric regression
Variance reduction.
Issue Date: 2011
Citation: Chen, S.X.,Tang, C.Y. (2011). Nonparametric regression with discrete covariate and missing values. Statistics and its Interface 4 (4) : 463-474. ScholarBank@NUS Repository.
Abstract: We consider nonparametric regression with a mixture of continuous and discrete explanatory variables where realizations of the response variable may be missing. An imputation based nonparametric regression estimator is proposed. We show that the proposed approach leads to a leading order variance benefit, whereas smoothing the categorical variables gives a second order variance improvement. We also demonstrate the applications of the proposed approach through numerical simulations and two practical examples.
Source Title: Statistics and its Interface
URI: http://scholarbank.nus.edu.sg/handle/10635/105242
ISSN: 19387989
Appears in Collections:Staff Publications

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