This paper considers the issue of bootstrap resampling in panel datasets. The availability of datasets with large temporal and cross sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross sectional units with replacement. In ca...

Community detection helps us simplify the complex configuration of networks, but communities are reliable only if they are statistically significant. To detect statistically significant communities, a common approach is to resample the original network and analyze the communities. But resampling assumes independence between samples, while the components of a network are inherently dependent. Th...

In this report a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms based on resampling quality and on computational complexity. Using extensive Monte Carlo simulations the th...

Euclidean distance -nearest neighbor ( -NN) classifiers are simple nonparametric classification rules. 5 5 Bootstrap methods, widely used for estimating the expected prediction error of classification rules, are motivated by the objective of calculating the ideal bootstrap estimate of expected prediction error. In practice, bootstrap methods use Monte Carlo resampling to estimate the ideal boot...

Statistical resampling methods have become feasible for parametric estimation, hypothesis testing, and model validation now that the computer is a ubiquitous tool for statisticians. This essay focuses on the resampling technique for parametric estimation known as the Jackknife procedure. To outline the usefulness of the method and its place in the general class of statistical resampling techniq...

Statistical resampling methods have become feasible for parametric estimation, hypothesis testing, and model validation now that the computer is a ubiquitous tool for statisticians. This essay focuses on the resampling technique for parametric estimation known as the Jackknife procedure. To outline the usefulness of the method and its place in the general class of statistical resampling techniq...

In this paper, we investigate a resampling approach for domain adaptation from a resource-rich domain (patent domain) to a resource-scarce target domain (newspaper domain) in Statistical Machine Translation (SMT). We propose two resampling methods for domain adaptation in SMT: random resampling and resampling for instance weighting. The random resampling randomly adds sentence pairs from the re...