Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions Academic Article

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.

authors

• Li, Qi

published proceedings

• JOURNAL OF BUSINESS & ECONOMIC STATISTICS

author list (cited authors)

• Li, Q. i., Lin, J., & Racine, J. S.

citation count

• 63

complete list of authors

• Li, Qi||Lin, Juan||Racine, Jeffrey S

publication date

• January 2013

publisher

• Taylor & Francis  Publisher

published in

• Journal of Business and Economic Statistics  Journal

keywords

• Cross-validation
• Data-driven
• Kernel Smoothing

Digital Object Identifier (DOI)

• 10.1080/07350015.2012.738955

start page

• 57

end page

• 65

volume

• 31

issue

• 1

URL

• http://dx.doi.org/10.1080/07350015.2012.738955