A consistent test for heteroscedasticity in semi-parametric regression with nonparametric variance function based on the kernel method

Autor:	Jin-Guan Lin, Xiao-Yi Qu
Rok vydání:	2012
Předmět:	Statistics and Probability Statistics::Theory Heteroscedasticity Statistics::Applications Nonparametric regression Statistics::Machine Learning Homoscedasticity Statistics Econometrics Statistics::Methodology Kernel regression Semiparametric regression Statistics Probability and Uncertainty Park test Goldfeld–Quandt test Mathematics Variance function
Zdroj:	Statistics. 46:565-576
ISSN:	1029-4910 0233-1888
DOI:	10.1080/02331888.2010.543464
Popis:	It is important to detect the variance heterogeneity in regression models. Heteroscedasticity tests have been well studied in parametric and nonparametric regression models. This paper presents a consistent test for heteroscedasticity for nonlinear semi-parametric regression models with nonparametric variance function based on the kernel method. The properties of the test are investigated through Monte Carlo simulations. The test methods are illustrated with a real example.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cd342f01fa2ec3f28567182a305b41e6 https://doi.org/10.1080/02331888.2010.543464 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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