A critical review is given of recent research activity on bootstrap and related procedures.
Theoretical work has shown the bootstrap approach to be a potentially powerful addition to …

The existence of high speed, inexpensive computing has made it easy to look at data in
ways that were once impossible. Where once a data analyst was forced to make restrictive …

The jackknife and bootstrap are the most popular data-resampling meth ods used in
statistical analysis. The resampling methods replace theoreti cal derivations required in …

This book gives a comprehensive account of density-based minimum distance methods and
their use in statistical inference. It covers statistical distances, density-based minimum …

The statistical bootstrap is one of the methods that can be used to calculate estimates of a
certain number of unknown parameters of a random process or a signal observed in noise …

This paper compares the minimum divergence estimator of Basu et al.(1998) to a competing
minimum divergence estimator which turns out to be equivalent to a method proposed from …

Given a set of baseline assumptions, a breakdown frontier is the boundary between the set
of assumptions which lead to a specific conclusion and those which do not. In a potential …

Many methods of cluster analysis provide techniques for subdividing a set of objects into a
suitable number of homogeneous classes on the basis of given numerical or qualitative …

In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is
proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with …

In testing of hypothesis, the robustness of the tests is an important concern. Generally, the
maximum likelihood-based tests are most efficient under standard regularity conditions, but …