Con dence intervals and tests for the location parameter are considered for time series generated by FEXP models. Since these tests mainly depend on the unknown fractional di erencing parameter d, the distribution of d̂ plays a major role. An exact closed form expresssion for the asymptotic variance of d̂ is given for FEXP models with cosine functions. It is shown that the variance increases line...

Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for ...

The external profile is among the first examined shape parameters of digital search trees in connection with the performance of unsuccessful search of a random query in the early 1970s. However, finer and important properties beyond the mean such as the variance and the limit law have remained unknown. In this extended abstract, we describe the first results for the asymptotic variance and the ...

Because the stationary bootstrap resamples data blocks of random length, this method has been thought to have the largest asymptotic variance among block bootstraps Lahiri [Ann. Statist. 27 (1999) 386–404]. It is shown here that the variance of the stationary bootstrap surprisingly matches that of a block bootstrap based on nonrandom, nonoverlapping blocks. This argument translates the variance...

Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for ...

We computationally investigate two approaches for uncertainty quantification in inverse problems for nonlinear parameter dependent dynamical systems. We compare the bootstrapping and asymptotic theory approaches for problems involving data with several noise forms and levels. We consider both constant variance absolute error data and relative error which produces non-constant variance data in o...

Because the stationary bootstrap resamples data blocks of random length, this method has been thought to have the largest asymptotic variance among block bootstraps (Lahiri, 1999, Ann. Statist.). It is shown here that the variance of the stationary bootstrap surprisingly matches that of a block bootstrap based on non-random, non-overlapping blocks. This argument translates the variance expansio...