The success of bootstrapping or replacing a human judge with a model (e.g., an equation) has been demonstrated in Paul Meehl's (1954) seminal work and bolstered by the results of several meta-analyses. To date, however, analyses considering different types of meta-analyses as well as the potential dependence of bootstrapping success on the decision domain, the level of expertise of the human ju...

As an important cryptographic primitive in cloud computing and outsourced computation, fully homomorphic encryption (FHE) is an animated area of modern cryptography. However, the efficiency of FHE has been a bottleneck that impeding its application. According to Gentry’s blueprint, bootstrapping, which is used to decrease ciphertext errors, is the most important process in FHE. However, bootstr...

Bootstrapping has been received a amount of attentions in many fields and achieved good results. While semantic lexicons also have been proved to be useful for many natural language processing tasks. This paper presents an approach to learn semantic lexicons using a new bootstrapping method which is based on Graph Mutual Reinforcement. The approach uses only unlabeled data and a few of seed wor...

In this paper we investigate to what extent the bootstrap can be applied conditional mean models, such as regression or time series when volatility of innovations is random and possibly non-stationary. fact, many economic financial displays persistent changes possible non-stationarity. However, theory for models has focused on deterministic unconditional variance little known about performance ...

The smooth bootstrap for estimating copula functionals in small samples is investigated. It can be used both to gauge the distribution of estimator question and augment data. Issues arising from kernel density estimation domain are addressed, such as how avoid bounded domain, which bandwidth matrix choose, smoothing carried out. Furthermore, we investigate impacts underlying dependence structur...

BACKGROUND Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible...