First order asymptotic validity is established for stationary bootstrapping of the realized volatility. This enables us to construct a bootstrapping confidence interval for integrated volatility. A Monte-Carlo experiment shows that stationary bootstrapping confidence interval is also valid in a...

In this work, the exact finite-sample distribution of the median absolute deviation about the median (MAD) of continuous random variables is derived. The main results are used for providing exact confidence intervals for the scale parameter of a location–scale model, which are robust to the...

This paper explores confidence intervals for the family of proportional reversed hazard distributions based on lower record values. The confidence intervals are validated as long as the sample is of size n≥3. The proposed procedure can be extended to the family of proportional hazard...

Empirical mortality data reveals that there is a finite age limit in the life span of humans, which means that it has a negative tail index. So far, there is a little literature on the confidence intervals for the tail index, especially for the negative tail index. In this paper, we construct...

In discrete distributions, the coverage probability and the expected length of an interval estimator often depend on the unknown parameter of interest. Some authors have suggested that “good” interval estimators should have mean coverage probability near the nominal level and small mean...

This paper concerns interval estimation for the difference of two dependent proportions. An order on the sample space is constructed using an inductive method; then the smallest one-sided 1−α confidence interval under the order is derived. This interval is admissible under the set inclusion...

Casella, Hwang and Robert, Statistica Sinica, 1993, consider a loss function that is a linear combination of the interval length and the indicator function that this interval includes the parameter of interest. They show that this leads to a confidence interval for the normal mean with...

This paper describes a 100 x (1 - [alpha]) percent confidence interval for the mean of a bounded random variable that holds for every sample size n and avoids the error of approximation that assuming normality induces.

We propose a fully Bayesian inference for semiparametric joint mean and variance models on the basis of B-spline approximations of nonparametric components. An efficient MCMC method which combines Gibbs sampler and Metropolis–Hastings algorithm is suggested for the inference, and the...

We provide a new bivariate distribution with beta marginal distributions, positive probability over the unit square, and correlations over the full range. We discuss its extension to three or more dimensions.