Uniform convergence rate of kernel estimation with mixed categorical and continuous data Academic Article

abstract

• We consider nonparametric estimation of regression functions with mixed categorical and continuous data, and we smooth both the discrete and continuous variables so that the approach does not suffer the sample splitting problem associated with the conventional frequency-based nonparametric estimation method. We derive the uniform convergence rate of the kernel regression function estimation with weakly dependent data. 2004 Elsevier B.V. All rights reserved.

authors

• Li, Qi

published proceedings

• ECONOMICS LETTERS

author list (cited authors)

• Li, Q., & Ouyang, D. S.

citation count

• 11

complete list of authors

• Li, Q||Ouyang, DS

publication date

• January 2005

publisher

• Elsevier  Publisher

published in

• Economics Letters  Journal

keywords

• Categorical Variable
• Nonparametric Smoothing
• Uniform Rate
• Weakly Dependent Data

Digital Object Identifier (DOI)

• 10.1016/j.econlet.2004.07.018

start page

• 291

end page

• 296

volume

• 86

issue

• 2

URL

• http://dx.doi.org/10.1016/j.econlet.2004.07.018