ON THE BOOTSTRAP OF THE MAXIMUM SCORE ESTIMATOR

By Jason Abrevaya, Jian Huang. 2005

148 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1691f777a8866594fa722e172e694ac8d7113a90

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In Statistics and Probability

Citations in the corpus

A Smoothed Least Squares Estimator for Threshold Regression Models (2007, result): “We take this as evidence of inconsistency, which would agree with results of Abrevaya and Huang (2005) for the unsmoothed maximum score procedure.”
Inconsistency of bootstrap: The Grenander estimator (2010, methodology): “= oP (1)−m1/3 n z { f̃n(t0)− f(t0) } + z(2) 2 f ′(t0 + αn(z))”
Bootstrap Methods in Econometrics (2018, unknown): “Abrevaya and Huang (2005) show that the nonparametric bootstrap does not provide a consistent estimate of this distribution.”
Robust inference for threshold regression models (2017, methodology): “Although the unconstrained model (1) encompasses both continuous and discontinuous models, the estimated regression function is almost surely discontinuous, since the probability that the LSE b ✓ fulfills the continuity restriction is zero.”
An Improved Bootstrap Test of Stochastic Dominance (2010, unknown): “case is asymptotically non-similar on the boundary because, for example, limN!1P(TN > z� �j� = (0;0)) 6=limN!1P(TN > z��j� = (0; 1 )): Now consider the following sequence of”
On m out of n Bootstrapping for Nonstandard M-Estimation With Nuisance Parameters (2006, background): “Abrevaya and Huang (2005) proved formally bootstrap inconsistency under conditions similar to those of Van der Vaart and Wellner’s (1996) theorem 3.”
Bootstrapping the Grenander estimator (2008, methodology): “We note that Abrevaya and Huang [1], in their Theorem 3, provide a general result on the unconditional limiting distribution of the bootstrap for argmax estimators that implies (2.”
Threshold Regression With a Threshold Boundary (2020, background): “A similar normalization also appears in the maximum score estimation (see, e.g., Abrevaya and Huang 2005) or the quasi-likelihood estimation (see, e.g., Klein and Spady 1993) of the binary choice model, but the threshold boundary does not include an error term and all variables in Z are observable.”
Control of generalized error rates in multiple testing (2007, background): “As a result, inconsistency of the bootstrap typically follows; see Abrevaya and Huang (2005). On the other hand, Delgado et al.”
Discrete Choice Modeling (2007, methodology): “…see Horowitz (1992), Charlier, Melenberg and van Soest (1995), Kyriazidou (1997) and Lee (1999) 13 Bootstrapping has been used to estimate the asymptotic covariance matrix for the maximum score estimator, however, Abrevaya and Huang (2005) have recently cast doubt on the validity of that approach.”