Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
Statistics and Computing
Implementing the Double Bootstrap
Computational Economics
Computational algorithms for double bootstrap confidence intervals
Computational Statistics & Data Analysis
Likelihood-free Bayesian estimation of multivariate quantile distributions
Computational Statistics & Data Analysis
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A new method is proposed for the correction of confidence intervals when the original interval does not have the correct nominal coverage probabilities in the frequentist sense. The proposed method is general and does not require any distributional assumptions. It can be applied to both frequentist and Bayesian inference where interval estimates are desired. We provide theoretical results for the consistency of the proposed estimator, and give two complex examples, on confidence interval correction for composite likelihood estimators and in approximate Bayesian computation (ABC), to demonstrate the wide applicability of the new method. Comparison is made with the double-bootstrap and other methods of improving confidence interval coverage.