Backfitting and smooth backfitting for additive quantile models
By Young K. Lee, Enno Mammen, Byeong U. Park. 2010
57 citations recorded by the source corpus.
In this paper, we study the ordinary backfitting and smooth backfitting as methods of fitting additive quantile models. We show that these backfitting quantile estimators are asymptotically equivalent to the corresponding backfitting estimators of the additive components in a specially-designed additive mean regression model. This implies that the theoretical properties of the backfitting quantile estimators are not unlike those of backfitting mean regression estimators. We also assess the finite sample properties of the two backfitting quantile estimators.
Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a8336f2fca1183c1ffb8354b0e76f4f90fec39b4
How researchers see this paper
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In Economics and Econometrics
In Management Science and Operations Research
In Statistics and Probability
- Eun Ryung Lee
- Young K. Lee
- Byeong U. Park
- Enno Mammen
- James W. Taylor
- Juan-Carlos Escanciano
- Dimitris Nicolas Politis
- Lijian Yang
Citations in the corpus
- Varying Coefficient Regression Models: A Review and New Developments (2015, methodology): “Recently, Lee et al. (2012b) studied the following fully extended version of model (3.”
- Additive Functional Regression for Densities as Responses (2020, unknown): “A crucial element of the proof of P(‖T‖op ≤ γ ) → 1 as n → ∞ for some constant 0 < γ < 1 is the bound ∫ [0,1]2 ( p̂jk(xj, xk) p̂j(xj)pk(xk) − pjk(xj, xk) pj(xj)pk(xk) )2 pj(xj)pk(xk) dxj dxk = op(1); see Mammen, Linton, and Nielsen (1999), Yu, Park, and Mammen (2008), Lee, Mammen, and Park (2010), a”