Markov Chain Marginal Bootstrap

By Xuming He, Feifang Hu. 2002

168 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c7a79e9ddb4f49a8004c8b494c4b90e26e4b1317

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Citations in the corpus

Specification Tests of Parametric Dynamic Conditional Quantiles (2010, unknown): “Resampling methods have been extensively used in the literature of quantile regression models, see, e.g., Hahn (1995), Horowitz (1998), Bilias, Chen and Ying (2000), Sakov and Bickel (2000) or He and Hu (2002).”
Censored Regression Quantiles (2003, background): “An alternative based on the Markov chain marginal bootstrap of He and Hu (2002) may also work well; substantial success for uncensored regression quantiles has been reported (Kocherginsky, He, and Mu 2003).”
ROBUST BACKTESTING TESTS FOR VALUE-AT-RISK MODELS (2011, methodology): “Chen and Ying (2000), Sakov and Bickel (2000) or He and Hu (2002) .”
Quantile Correlations and Quantile Autoregressive Modeling (2012, unknown): “In the context of quantile regression models, several bootstrap methods have been proposed; see, for example , He and Hu (2002) and Kocherginsky, He, and Mu (2005).”
Inference on the Quantile Regression Process (2000, background): “Hahn (1995), Horowitz (1998), Bilias, Chen, and Ying (1999), He and Hu (1999). In Koenker and Machado (1999) some initial steps have been taken toward a theory of inference based on the entire quantile regression process. These steps have clari ed the close tie to classical Kolmogorov-Smirnov goodne”
Asymmetric Loss Functions for Forecasting in Criminal Justice Settings (2011, background): “There are a number of very interesting bootstrap options that were also examined (Kocherginsky et at., 2005; He and Hu, 2002; Parsen et al., 1994; Bose and Chatterjee, 2003).”
Finite Sample Inference for Quantile Regression Models (2006, unknown): “…method of Parzen, Wei, and Ying (1994).1 In sharp contrast to these methods, the finite sample approach does not 1The finite sample method should not be confused with the Gibbs bootstrap proposed in He and Hu (2002) who propose a computationally attractive variation on Parzen, Wei, and Ying (1994).”
Practical Confidence Intervals for Regression Quantiles (2005, methodology): “Most importantly, it was shown by He and Hu (2002) that, under model (1.3), the sequence β(k) (k = 1, . . . ,K) is an asymptotically multivariate AR(1) process n1/2(β(k) − β̂τ ) = Ann1/2(β(k−1) − β̂τ ) + un,k, (3.2) and the sample variance of β(k) consistently approximates Vτ for large n and K.”