During this period, robustness became anchored in formal diagnostic tools and adaptive estimation. The development of influence-based diagnostics enabled precise assessment of how individual observations affect estimators, while iterative weighting schemes produced computationally practical robust estimators. Nonparametric smoothing through cross-validation provided a reliable guard against overfitting and noise, and robust approaches for linear state estimation extended robustness into dynamic settings.