Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.

A Methodology for Neural Spatial Interaction Modeling

Abstract: This paper attempts to develop a mathematically rigid and unified framework for neural spatial interaction modeling. Families of classical neural network models, but also less classical ones such as product unit neural network ones are considered for the cases of unconstrained and singly constrained spatial interaction flows. Current practice appears to suffer from least squares and normality assumptions that ignore the true integer nature of the flows and approximate a discrete-valued process by an almost certainly misrepresentative continuous distribution. To overcome this deficiency we suggest a more suitable estimation approach, maximum likelihood estimation under more realistic distributional assumptions of Poisson processes, and utilize a global search procedure, called Alopex, to solve the maximum likelihood estimation problem. To identify the transition from underfitting to overfitting we split the data into training, internal validation and test sets. The bootstrapping pairs approach with replacement is adopted to combine the purity of data splitting with the power of a resampling procedure to overcome the generally neglected issue of fixed data splitting and the problem of scarce data. In addition, the approach has power to provide a better statistical picture of the prediction variability, Finally, a benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained neural network model versions in terms of generalization performance measured by Kullback and Leibler's information criterion.

Teaching Statistics through Resampling

Abstract: Summary This paper describes experiences of teaching statistics without mathematical theory but using computer-intensive re-sampling methods. The method is relevant to statistics teaching at all levels.

Upper Bounds on the True Coverage of Bootstrap Percentile Type Confidence Intervals

Abstract: Since its inception, a major use of the bootstrap methodology has been in the construction of approximate nonparametric confidence intervals. As evidenced by many spirited discussions over the past few years, the best way of constructing these intervals has not been resolved. In particular, empirical studies have shown that many of these intervals have disappointing finite sample coverage probabilities. The purpose of this article is to show that intervals based on percentiles of the bootstrap distribution have bounds on their finite sample coverage probabilities. Depending on the functional of interest and the distribution of the data, these bounds can be quite low. We argue that these bounds are valid even for methods that are asymptotically second-order accurate. These results are useful to researchers who are contemplating using this type of confidence interval when the sample size is small. These bounds are computed for several examples including the moments and quantiles of several distribu...

Constraint projections for ensemble learning

Abstract: It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods obtain diverse individual learners through resampling the instances or features. In this paper, we propose an alternative way for ensemble construction by resampling pairwise constraints that specify whether a pair of instances belongs to the same class or not. Using pairwise constraints for ensemble construction is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints. We solve this problem with a two-step process. First, we transform the original instances into a new data representation using projections learnt from pairwise constraints. Then, we build the base classifiers with the new data representation. We propose two methods for resampling pairwise constraints following the standard Bagging and Boosting algorithms, respectively. Extensive experiments validate the effectiveness of our method.