This paper discusses a bootstrap-based test that checks if finite moments exist and indicates cases of possible misapplication. It notes, in fact, that a procedure for finding the smallest power to which a sample needs to be raised, such that the test rejects a hypothesis that the corresponding...

In risk management areas such as reinsurance, the need often arises to construct a confidence interval for a quantile in the tail of the distribution; for example, there is high probability that the sample maximum lies near or below the quantile. While different methods, including subsample...

Different bootstrap methods and estimation techniques for inference for structural vector autoregressive (SVAR) models identified by conditional heteroskedasticity are reviewed and compared in a Monte Carlo study. The model is a SVAR model with generalized autoregressive conditional...

The bootstrap, introduced by Efron (1982), has become a very popular method for estimating variances and constructing confidence intervals. A key insight is that one can approximate the properties of estimators by using the empirical distribution function of the sample as an approximation for...

In the Big Data era, ever-increasing RDF data have reached a scale in billions of entities and brought challenges to the problem of entity linkage on the Semantic Web. Although millions of entities, typically denoted by URIs, have been explicitly linked with owl:sameAs, potentially coreferent...

In this article, two methods are proposed to give the interval estimation for two parameters exponential distribution under multiple type-II censoring on complex case. Fei and Kong (1994), Balasubramanian and Balakrishnan (1992) use approximate maximum likelihood estimator to construct interval...

This article concerns the application of bootstrap methodology to construct a likelihood-based confidence region for operating conditions associated with the maximum of a response surface constrained to a specified region. Unlike classical methods based on the stationary point, proper...

We study inference for the local innovations of It\^o semimartingales. Specifically, we construct a resampling procedure for the empirical CDF of high-frequency innovations that have been standardized using a nonparametric estimate of its stochastic scale (volatility) and truncated to rid the...

Applied researchers often want to make inference for the difference of a given performance measure for two investment strategies. In this paper, we consider the class of performance measures that are smooth functions of population means of the underlying returns; this class is very rich and...