APPROXIMATING THE DISTRIBUTION OF M-ESTIMATORS IN LINEAR MODELS BY RANDOMLY WEIGHTED BOOTSTRAP

By Yaning Yang. 2008

53 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6ede3df871c5d70dc64b53ae561ff74b6f0e3416

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Caleb H. Miles
2. Phyllis Kanki
3. Seema Meloni
4. E. T. Tchetgen Tchetgen
5. Razieh Nabi
6. Ilya Shpitser
7. Edward H. Kennedy
8. Iv'an D'iaz

Citations in the corpus

• Quantile Correlations and Quantile Autoregressive Modeling (2012, unknown): “Hence, we consider a bootstrap approach from Rao and Zhao (1992) by introducing a series of random weights to the loss function, see also Jin, Ying, and Wei (2001), Feng, He, and Hu (2011), and Li, Leng, and Tsai (2013).”
• On Partial Identification of the Pure Direct Effect (2015, methodology): “Throughout, inference is performed using maximum likelihood for point estimation and a weighted bootstrap (Rao and Zhao, 1992; van der Vaart and Wellner, 1996) for confidence intervals, which appropriately accounts for the rare outcome.”