In simulations of partial differential equations using particle-in-cell (PIC) methods, it is often advantageous to resample the particle distribution function to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. We introduce an algorithm for particle resampling called Moment Preserving Contrained Resampling (MPCR). The general algorithm partitions the syst...

Cardiovascular disease that causes heart failure is one of the diseases with highest mortality rate in world. Therefore, there a need for an accurate model to classify based on clinical information and lifestyle patients disease, as alternative solution administering appropriate drugs. This study compared classification living deceased patient using random forest method when resampling techniqu...

We show that that the jackknife variance estimator vjack and the the infinitesimal jackknife variance estimator are asymptotically equivalent if the functional of interest is a smooth function of the mean or a smooth trimmed L-statistic. We calculate the asymptotic variance of vjack for these functionals.

The inequality originates from studies of the jackknife estimate of variance . Several interesting proofs [see, e.g ., Vitale (1984)] and applications [see, e.g., Steele (1981, 1982)] highlight the depth and usefulness of the Efron-Stein inequality. The purpose of this short note is to point out an application in density estimation . We consider in particular a density estimate fn of a density ...

The jackknife variance estimator and the the infinitesimal jackknife variance estimator are shown to be asymptotically equivalent if the functional of interest is a smooth function of the mean or a trimmed L-statistic with Hölder continuous weight function.

Image resampling method using color edge-directed interpolation has been developed. It uses color image gradient to perform the interpolation across image gradient rather than along image gradient. The developed combined method takes color low resolution image and grayscale high resolution image obtained by a non-linear image resampling method as an input. It includes consecutive calculation st...

Neural networks must be constructed and validated with strong empirical dependence, which is difficult under conditions of sparse data. This paper examines the most common methods of neural network validation along with several general validation methods from the statistical resampling literature as applied to function approximation networks with small sample sizes. It is shown that an increase...

The burgeoning eld of genomics, and in particular microarray experiments, have revived interest in both discriminant and cluster analysis, by raising new methodological and computational challenges. The present paper discusses applications of resampling methods to problems in cluster analysis. A resampling method, known as bagging in discriminant analysis, is applied to increase clustering accu...

Resampling techniques such as bagging are often used in supervised learning to produce more accurate classifiers. In this work, we show that multiple-instance learning admits a different form of resampling, which we call “shuffling.” In shuffling, we resample instances in such a way that the resulting bags are likely to be correctly labeled. We show that resampling results in both a reduction o...

Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications, it is desirable to have resamples that preserve certain non-Poisson properties, like refractory periods and bursting, and that are also robust to trial-to-trial variability. Pattern jitter is a resampling technique that accomplishes this by preserving the recent spiking his...