A consistent test for heteroscedasticity in nonparametric regression based on the kernel method

By Holger Dette. 2002

62 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9e4d6a26d1571187957e31ddb06075f440ea82a4

How researchers see this paper

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

In Artificial Intelligence

1. I. Van Keilegom
2. Natalie Neumeyer
3. Holger Dette

In Statistics and Probability

1. I. Van Keilegom
2. Natalie Neumeyer
3. Holger Dette
4. Wenceslao González-Manteiga
5. Rosa M. Crujeiras
6. Stanislav Volgushev
7. Juan Carlos Pardo-Fernández
8. P. Groeneboom

Citations in the corpus

• A new test for the parametric form of the variance function in non‐parametric regression (2007, methodology): “Recently Dette (2002) and Liero (2003) used estimates for the L2-distance between the variance estimators in both models—the underlying heteroscedastic model and the hypothetical homoscedastic model—as test statistic.”
• Variance estimation in nonparametric regression via the difference sequence method (2007, unknown): “The Annals of Statistics 2007, Vol. 35, No. 5, 2219–2232 DOI: 10.1214/009053607000000145 © Institute of Mathematical Statistics, 2007”