On spline estimators and prediction intervals in nonparametric regression

By Kjell A. Doksum, Ja-Yong Koo. 2000

50 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8658ce5e6cd053022be7e6a4e398f4379dc93457

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. S. Lee
2. Mi-Ok Kim
3. Joel L. Horowitz
4. Byeong U. Park
5. Hohsuk Noh
6. Eun Ryung Lee
7. Léopold Simar
8. Paul Wilson

In Statistics and Probability

1. Joel L. Horowitz
2. S. Lee
3. Eun Ryung Lee
4. Byeong U. Park
5. Hohsuk Noh
6. Mi-Ok Kim
7. D. Chetverikov
8. Victor Chernozhukov

Citations in the corpus

• Nonparametric Estimation of an Additive Quantile Regression Model (2004, methodology): “Bsplines were used for the first-stage with κn = 4, which is equal to κ̂n + 1, where κ̂n = 3 minimizes the following Schwarz-type information criterion (see He and Shi (1996) and Doksum and Koo (2000))”
• Model Selection via Bayesian Information Criterion for Quantile Regression Models (2014, background): “Such examples can be found in He and Shi (1996), Doksum and Koo (2000), Horowitz and Lee (2005) and Kim (2007).”
• Quantile regression with varying coefficients (2007, background): “De Gooijer and Zerom [6] and Doksum and Koo [7] considered local polynomials and polynomial splines with additive models.”