Weighted envelope estimation to handle variability in model selection
针对包络方法中维度选择带来的波动性，提出加权包络估计器，通过理论证明和残差自助法验证其渐近方差的有效性，模拟和实证分析展示了其效用。
Envelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our estimator, and the validity of the residual bootstrap for estimating its asymptotic variance is established. A simulation study and real-data analysis illustrate the utility of our weighted envelope estimator.