Finite-Sample Risk Approximation and Risk-Consistent Tuning for Generalized Ridge Estimation in Nonlinear Models: Controlling Extreme Realizations
非线性模型中广义岭估计的有限样本风险近似与风险一致调参
Maximum likelihood estimation in nonlinear models can exhibit substantial instability in finite samples when the data provide limited information about certain parameters. Such instability is driven by rare but extreme realizations of the estimator, which can dominate mean squared error (MSE) and lead to poor performance of conventional estimators. To address this issue, we consider ridge estimators that directly target MSE through regularization and thereby control extreme…