Robust tests in nonlinear regression models

DOI10.1016/0378-3758(95)00194-8zbMath0857.62027OpenAlexW2068802118MaRDI QIDQ1817297

Marianthi Markatou, George Manos

Publication date: 6 March 1997

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(95)00194-8

zbMATH Keywords

heteroscedasticitysimulation resultsnonlinear modelsinfluence functionstau-testscore-type testsaligned GM-testdrop-in-dispersion testing proceduressubhypothesesWald-type testing procedures

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Robustness and adaptive procedures (parametric inference) (62F35) General nonlinear regression (62J02) Asymptotic properties of parametric tests (62F05)

Related Items

Robust inference for nonlinear regression models, A robust adaptive-to-model enhancement test for parametric single-index models, Optimum robust testing in linear models

Uses Software

• nlmdl

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Accounting for intrinsic nonlinearity in nonlinear regression parameter inference regions
• Functional relations, random coefficients, and nonlinear regression with application to kinetic data
• Robust estimation in heteroscedastic linear models
• A local breakdown property of robust tests in linear regression
• Invariants and likelihood ratio statistics
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Breakdown Robustness of Tests
• General m-esttmators and applications to bounded influence estimation for non-linear regression
• Tests of linear hypotheses based on regression rank scores
• A Comparison Between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear Model
• Some New Estimation Methods for Weighted Regression When There Are Possible Outliers
• Diagnostics and Robust Estimation When Transforming the Regression Model and the Response
• Heteroscedastic Nonlinear Regression
• Robust analysis of variance based upon a likelihood ratio criterion
• Asymptotic behavior of general M-estimates for regression and scale with random carriers
• Nonlinear Regression
• Modern trends in the theory of robustness²
• Fitting Heteroscedastic Regression Models
• Bounded Influence and High Breakdown Point Testing Procedures in Linear Models
• Preface