Model Robust Confidence Intervals Using Maximum Likelihood Estimators
Copyright policy )Model Robust Confidence Intervals Using Maximum Likelihood Estimators
Summary: Standard large-sample confidence intervals about a maximum likelihood estimator \({\hat \theta}\) are two-thirds robust; i.e. when the parametric model is imperfect \({\hat \theta}\) often remains consistent and asymptotically normal. The confidence intervals are invalidated only because the third necessary condition, consistency of the variance estimator, fails. The 'delta method' provides a simple alternative variance estimator which remains consistent under more general conditions and provides robust large-sample confidence intervals.
Parametric tolerance and confidence regions, asymptotically normal, variance estimator, maximum likelihood estimator, Robustness and adaptive procedures (parametric inference), robustness, delta method, large-sample theory, consistent
Parametric tolerance and confidence regions, asymptotically normal, variance estimator, maximum likelihood estimator, Robustness and adaptive procedures (parametric inference), robustness, delta method, large-sample theory, consistent
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