Nonparametric comparison of regression curves: An empirical process approach

By Holger Dette, Natalie Neumeyer. 2003

148 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ecd9d6bcee9e13c025b387e8756c954a7554dba3

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. Wenceslao González-Manteiga
2. Stanislav Volgushev
3. Frank Bretz
4. Kathrin Möllenhoff
5. I. Van Keilegom
6. P. Groeneboom
7. Cécile Durot
8. Hendrik P. Lopuhaä

In Statistics and Probability

1. Wenceslao González-Manteiga
2. Changliang Zou
3. P. Groeneboom
4. Cécile Durot
5. I. Van Keilegom
6. Stanislav Volgushev
7. Juan Carlos Pardo-Fernández
8. Peihua Qiu

Citations in the corpus

• Nonparametric Tests for Treatment Effect Heterogeneity (2008, unknown): “The closest papers in terms of focus to the current paper are those by H¨ardle and Marron (1990), Neumeyer and Dette (2003) and Pinkse and Robinson (1995).”
• Equivalence of Regression Curves (2018, methodology): “In the context of comparing curves, Dette and Neumeyer (2001) provided a nonparametric test for the null hypothesis that the L2-distance between two regression functions is large against the alternative that it is small.”
• An updated review of Goodness-of-Fit tests for regression models (2013, unknown): “…be built on the following empirical regression process: αN(x) = 1 N n1 ∑ i=1 f1iI(X1i ≤ x) − 1 N n2 ∑ i=1 f2iI(X2i ≤ x), with fli = N nl Yli − m̂(Xli) f̂l(Xli) , for l = 1,2 and i = 1, . . . , nl , f̂l being the corresponding kernel density estimator for the covariate (see Neumeyer and Dette 2003).”
• NONPARAMETRIC CONTROL CHART FOR MONITORING PROFILES USING CHANGE POINT FORMULATION AND ADAPTIVE SMOOTHING (2009, background): “A recent review on this topic is given by Neumeyer and Dette (2003) .”
• Testing equality of functions under monotonicity constraints (2012, methodology): “ possibly heteroscedastic errors, [34] consider a test based on the estimation of the L 2-distance between the two regression curves and generalized the method to the case of k-samples, k≥ 2, whereas [35] developed a test calibrated by the bootstrap, which is based on the difference of two marked emp”
• Testing for the equality of k regression curves (2007, methodology): “In Neumeyer and Dette (2003) only the homoscedastic case is considered.”