Bootstrap consistency for general semiparametric M-estimation

Cheng, Guang; Huang, Jianhua Z.

doi:10.1214/10-aos809

Cited by 118 publications

(92 citation statements)

References 44 publications

(145 reference statements)

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“…However, when these bootstrap procedures are applied to the change-point model (1.2), they yield invalid confidence intervals (CIs) for ζ 0 (see Section 4). The failure of the usual bootstrap methods in non-standard problems has been documented in the literature; see Abrevaya and Huang (2005) and Sen, Banerjee and Woodroofe (2010) for situations giving rise to n −1/3 asymptotics; also see Bose and Chatterjee (2001) and Cheng and Huang (2010) for M -estimation problems. The performance of different bootstrap methods for the Cox model (1.2) has not been investigated in the literature.…”

Section: Introductionmentioning

confidence: 97%

Bootstrapping a change-point Cox model for survival data

Xu¹,

Sen²,

Ying³

2014

Electron. J. Statist.

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This paper investigates the (in)-consistency of various bootstrap methods for making inference on a change-point in time in the Cox model with right censored survival data. A criterion is established for the consistency of any bootstrap method. It is shown that the usual nonparametric bootstrap is inconsistent for the maximum partial likelihood estimation of the change-point. A new model-based bootstrap approach is proposed and its consistency established. Simulation studies are carried out to assess the performance of various bootstrap schemes.

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Section: Introductionmentioning

confidence: 97%

Bootstrapping a change-point Cox model for survival data

Xu¹,

Sen²,

Ying³

2014

Electron. J. Statist.

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“…Likewise, we have Lemma D.1 (Cheng and Huang (2010), Lemma 3). Given a vector valued statistic ∆ n depending on both c 1 , .…”

Section: Part (Iv)mentioning

confidence: 67%

Estimation and Inference for Linear Panel Data Models Under Misspecification When BothnandTare Large

Galvao¹,

Kato²

2014

Journal of Business & Economic Statistics

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Abstract. This paper considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, n, and the number of time periods, T , are large. We first clarify the probability limit of the FE estimator and argue that this probability limit can be regarded as a pseudo-true parameter. We then establish the asymptotic distributional properties of the FE estimator around the pseudo-true parameter when n and T jointly go to infinity. Notably, we show that the FE estimator suffers from the incidental parameters bias of which the top order is O(T −1 ), and even after the incidental parameters bias is completely removed, the rate of convergence of the FE estimator depends on the degree of model misspecification and is either (nT ) −1/2 or n −1/2 . Second, we establish asymptotically valid inference on the (pseudo-true) parameter. Specifically, we derive the asymptotic properties of the clustered covariance matrix (CCM) estimator and the cross section bootstrap, and show that they are robust to model misspecification. This establishes a rigorous theoretical ground for the use of the CCM estimator and the cross section bootstrap when model misspecification and the incidental parameters bias (in the coefficient estimate) are present. We conduct Monte Carlo simulations to evaluate the finite sample performance of the estimators and inference methods, together with a simple application to the unemployment dynamics in the U.S.

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“…In this section we present the bootstrap technique which could be used for improving the finite sample performance of the robust identification and approximating the unknown probability distribution [53,54]. Typically, the approximation can be performed by different estimators of the data distribution.…”

Section: Bootstrap Estimate By Penalized Smoothing Splinesmentioning

confidence: 99%