The estimating function bootstrap

By Feifang Hu, John D. Kalbfleisch. 2000

96 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/657801b49efb851a70bbe4cbe7fa232226d3c786

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In Statistics and Probability

Citations in the corpus

Bootstrap Tests for Regression Models (2009, unknown): “often superior, in terms of accuracy and computational cost, to standard bootstrap methods; see Hu and Kalbfleisch (2000). There is, however, an important restriction on the availability of the estimating function bootstrap; the data must be independently distributed.”
Bootstrap Methods in Econometrics (2006, unknown): “Some interesting ones that potentially have econometric applications are discussed in Davison, Hinkley, and Young (2003), Hu and Kalbfleisch (2000), Lahiri (2003), Lele (2003), and Shao (2003).”
Fast and wild: Bootstrap inference in Stata using boottest (2018, background): “Hu and Zidek (1995) and Hu and Kalbfleisch (2000) showed how to apply the pairs bootstrap to scores. Kline and Santos (2012) applied the wild bootstrap instead, producing what they dub the score bootstrap.”
A Score Based Approach to Wild Bootstrap Inference (2012, unknown): “n i i s In the linear model, this procedure corresponds to that of Hu and Zidek (1995). For nonlinear problems, Hu and Kalbfleisch (2000) propose a closely related approach that additionally resamples the Hessian.”
Inference Functions and Quadratic Score Tests (2003, methodology): “That is, a bootstrap resampling strategy (Efron, 1987; Hall and Horowitz, 1996; Hu and Kalbfleisch, 2000) can be a simple and effective way to achieve higherorder accuracy for test size, and it works effectively for relatively small sample sizes in our case.”
Pair Copula Constructions for Multivariate Discrete Data (2012, methodology): “However, there is also strong theoretical justification for using methods such as the jackknife (Joe 1997) or bootstrap (Hu and Kalbfleisch 2000) to estimate V .”