Markov Chain Marginal Bootstrap
- 1 September 2002
- journal article
- research article
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 97 (459) , 783-795
- https://doi.org/10.1198/016214502388618591
Abstract
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 esti...Keywords
This publication has 15 references indexed in Scilit:
- The estimating function bootstrapThe Canadian Journal of Statistics / La Revue Canadienne de Statistique, 2000
- Asymptotic distributions of the maximal depth estimators for regression and multivariate locationThe Annals of Statistics, 1999
- A general Bahadur representation of M-estimators and its application to linear regression with nonstochastic designsThe Annals of Statistics, 1996
- Bootstrap confidence intervalsStatistical Science, 1996
- A bootstrap based on the estimating equations of the linear modelBiometrika, 1995
- Tests of linear hypotheses based on regression rank scoresJournal of Nonparametric Statistics, 1993
- Bootstrapping $M$-Estimators of a Multiple Linear Regression ParameterThe Annals of Statistics, 1992
- Bootstrap Procedures under some Non-I.I.D. ModelsThe Annals of Statistics, 1988
- Bootstrapping Regression ModelsThe Annals of Statistics, 1981
- Bootstrap Methods: Another Look at the JackknifeThe Annals of Statistics, 1979