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We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging propos...
Adaptive confidence intervals for regression functions are constructed under shape constraints of monotonicity and convexity. A natural benchmark is established for the minimum expected length of conf...
Consider a linear regression model with n-dimensional response vector, p-dimensional regression parameter beta and independent normally distributed errors. Suppose that the parameter of interest is th...
When computing a confidence interval for a binomial proportion p one must choose between using an exact interval, which has a coverage probability of at least 1-{\alpha} for all values of p, and a sho...
In Adaptive Markov Chain Monte Carlo (AMCMC) simulation, classical estimators of asymptotic variances are inconsistent in general. In this work we establish that despite this inconsistency, confidence...
Monte Carlo methods are used to approximate the means,? of random variablesY, whose distributions are not known explicitly. The key idea is that the average of a random sample,Y1,...,Yn, tends to 礱sn...
Many statistical data are imprecise due to factors such as mea-surement errors, computation errors, and lack of information. In such cases, data are better represented by intervals rather thanby singl...
Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the orig...
The 'standard' confidence interval for a Poisson parameter is only one of a number of estimation intervals based on the chi-square distribution that may be used in the estimation of the mean or mean r...
Asymptotic multivariate normality for the subseries values of a general statistic form a stationary sequence - with applications to nonparametric confidence intervals
We consider the construction of unconditional bootstrap- t prediction intervals for stationary time series. Our approach relies on the sieve bootstrap resampling scheme introduced by Biihlmann. Ba...
In this paper, we develop a general theory on the coverage probability of random inter- vals defined in terms of discrete random variables with continuous parameter spaces. The theory shows that the...
For estimating a lower bounded location or mean parameter for a symmetric and logconcave density, we investigate the frequentist performance of the $100(1-alpha)%$ Bayesian HPD credible set associated...
In this short note, we show how to use concentration inequalities in order to build exact confidence intervals for the Hurst parameter associated with a one-dimensional fractional Brownian motion.
We consider independent random variables (r.v.'s) with a common mean $mu$ that either satisfy Lindeberg's condition, or are symmetric around $mu$. Present forms of existing functional central limit th...

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