A Unified Robust Inference Framework for Univariate and Multivariate  Nonlinear Statistical Models

Sreenivasulu Arigela, M. Venkataramanaiah, B .Sarojamma

A Unified Robust Inference Framework for Univariate and Multivariate Nonlinear Statistical Models

Authors

Sreenivasulu Arigela, M. Venkataramanaiah, B .Sarojamma

Keywords:

Nonlinear regression; robust estimation; MM-estimators; sandwich covariance; wild bootstrap; influence function; multivariate nonlinear models; heteroscedasticity

Abstract

Nonlinear regression models underpin quantitative work across the biological, physical,pharmacological, and economic sciences, yet the inferential machinery most practitioners rely on—ordinary nonlinear least squares (NLS) together with the classical asymptotic covariance

References

1)Andrews, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3), 817–858.

2)Bates, D. M., & Watts, D. G. (1980). Relative curvature measures of nonlinearity. Journal of the Royal Statistical Society, Series B, 42(1), 1–25.

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Published

2026-05-20

How to Cite

Sreenivasulu Arigela, M. Venkataramanaiah, B .Sarojamma. (2026). A Unified Robust Inference Framework for Univariate and Multivariate Nonlinear Statistical Models . Journal of Computational Analysis and Applications (JoCAAA), 35(5), 382–400. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5838