Test for the choice of approximative models in nonlinear regression when the variance is unknown

DOI10.1080/02331888308802396zbMath0808.62059OpenAlexW1989255867MaRDI QIDQ4324716

F. Brodeau

Publication date: 28 February 1995

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888308802396

zbMATH Keywords

simulationgoodness-of-fit testunknown variancecomparison testlast squares estimators

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric hypothesis testing (62F03) General nonlinear regression (62J02)

Related Items (6)

Model checks for parametric regression models ⋮ Testing model assumptions in functional regression models ⋮ Testing linearity of regression models with dependent errors by kernel based methods ⋮ Validation of linear regression models ⋮ A consistent test for the functional form of a regression based on a difference of variance estimators ⋮ A consistent test for heteroscedasticity in nonparametric regression based on the kernel method

Cites Work

• Unnamed Item
• Bandwidth choice for nonparametric regression
• Residual variance and residual pattern in nonlinear regression
• Least Squares Estimation when the Covariance Matrix and Parameter Vector are Functionally Related
• Asymptotic results for parametric estimation in inadequate two phases regression models
• Asymptotic Properties of Non-Linear Least Squares Estimators

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