Sequential determination of the number of bootstrap samples

The primary purpose of this paper is that of developing a sequential Monte Carlo approximation to an ideal bootstrap estimate of the parameter of interest. Using the concept of fixed-precision approximation, we construct a sequential stopping rule for determining the number of bootstrap samples to be taken in order to achieve a specified precision of the Monte Carlo approximation. It is shown that the sequential Monte Carlo approximation is asymptotically efficient in the problems of estimation of the bias and standard error of a given statistic. Efficient bootstrap resampling is discussed and a numerical study is carried out for illustrating the obtained theoretical results.