Testing for Heteroscedasticity and/or Correlation in Nonlinear Models with Correlated Errors

DOI10.1081/STA-120028373zbMath1102.62070OpenAlexW2083544686MaRDI QIDQ3155259

Jin-Guan Lin, Bo-Cheng Wei

Publication date: 14 January 2005

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1081/sta-120028373

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric hypothesis testing (62F03) Hypothesis testing in multivariate analysis (62H15) General nonlinear regression (62J02)

Related Items (13)

Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors ⋮ Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data ⋮ The multiplicative heteroscedastic von Bertalanffy model ⋮ Asymptotic properties of the score test for varying dispersion in exponential family nonlinear models ⋮ Heteroscedasticity and/or autocorrelation checks in longitudinal nonlinear models with elliptical and AR(1) errors ⋮ Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors ⋮ Diagnostics for elliptical linear mixed models with first-order autoregressive errors ⋮ Diagnostics for skew-normal nonlinear regression models with AR(1) errors ⋮ The Performance of Robust Two-Stage Estimator in Nonlinear Regression with Autocorrelated Error ⋮ Heteroscedasticity diagnostics for \(t\) linear regression models ⋮ Heteroscedasticity diagnostics in two-phase linear regression models ⋮ Diagnostics for a Linear Model with First-Order Autoregressive Symmetrical Errors ⋮ Testing for Heteroscedasticity and/or Autocorrelation in Longitudinal Mixed Effect Nonlinear Models with AR(1) Errors

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• Score Tests for Regression Models
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• Testing for Heteroscedasticity in Nonlinear Regression Models
• Use of Modified Profile Likelihood for Improved Tests of Constancy of Variance in Regression
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation

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