Please use this identifier to cite or link to this item: https://doi.org/10.1080/07350015.2012.738955
Title: Optimal bandwidth selection for nonparametric conditional distribution and quantile functions
Authors: Li, Q.
Lin, J. 
Racine, J.S.
Keywords: Cross-validation
Data-driven
Kernel smoothing
Issue Date: 2013
Citation: Li, Q., Lin, J., Racine, J.S. (2013). Optimal bandwidth selection for nonparametric conditional distribution and quantile functions. Journal of Business and Economic Statistics 31 (1) : 57-65. ScholarBank@NUS Repository. https://doi.org/10.1080/07350015.2012.738955
Abstract: We propose a data-driven least-square cross-validation method to optimally select smoothing parameters for the nonparametric estimation of conditional cumulative distribution functions and conditional quantile functions. We allow for general multivariate covariates that can be continuous, categorical, or a mix of either. We provide asymptotic analysis, examine finite-sample properties via Monte Carlo simulation, and consider an application involving testing for first-order stochastic dominance of children's health conditional on parental education and income. This article has supplementary materials online. © 2013 American Statistical Association.
Source Title: Journal of Business and Economic Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/128733
ISSN: 07350015
DOI: 10.1080/07350015.2012.738955
Appears in Collections:Staff Publications

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