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Markov chain marginal bootstrap

delete2002-09-01
delete100
PRE
AI
X
Xuming He
F
Feifang Hu
DOI：10.1198/016214502388618591delete
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Abstract

Abstract

En 中文
Markov chain marginal bootstrap (MCMB) is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of M estimators of linear regression. The MCMB method distinguishes itself from the usual bootstrap methods in two important aspects: it involves solving only one-dimensional equations for parameters of any dimension and produces a Markov chain rather than a (conditionally) independent sequence. It is designed to alleviate computational burdens often associated with bootstrap in high-dimensional problems. The validity of MCMB is established through asymptotic analyses and illustrated with empirical and simulation studies for linear regression and generalized linear models.
Keywords:
asymptotic normality
confidence interval
generalized linear model
M estimator
maximum likelihood
regression
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Journal

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Journal of the American Statistical Association
IF:
3
Papers:
5.1K
Citations:
4.8W

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