Assessing the Adequacy of Variance Function in Heteroscedastic Regression Models

DOI10.1111/j.1541-0420.2007.00805.xzbMath1136.62037OpenAlexW2069768629WikidataQ51915538 ScholiaQ51915538MaRDI QIDQ5449923

Lan Wang, Xiao-Hua Andrew Zhou

Publication date: 19 March 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00805.x

zbMATH Keywords

kernel smoothingvariance functiongoodness-of-fit testpseudolikelihoodheteroscedastic errorsesterase count data

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric statistical resampling methods (62G09)

Related Items (15)

Pairwise distance-based heteroscedasticity test for regressions ⋮ Model checking in Tobit regression via nonparametric smoothing ⋮ Conditional variance model checking ⋮ \(DS\)-optimal designs for random coefficient first-degree regression model with heteroscedastic errors ⋮ Testing the parametric form of the conditional variance in regressions based on distance covariance ⋮ Accounting for uncertainty in heteroscedasticity in nonlinear regression ⋮ Empirical smoothing lack-of-fit tests for variance function ⋮ Unnamed Item ⋮ Minimum distance conditional variance function checking in heteroscedastic regression models ⋮ A homoscedasticity test for the accelerated failure time model ⋮ A semiparametric latent factor model for large scale temporal data with heteroscedasticity ⋮ Distance-covariance-based tests for heteroscedasticity in nonlinear regressions ⋮ A note on testing the regression functions via nonparametric smoothing ⋮ A model specification test for the variance function in nonparametric regression ⋮ Evaluating the adequacy of variance function using pairwise distances

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