We provide a short overview of Importance Sampling – a popular sampling tool used for Monte Carlo computing. We discuss its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations. We review the fundamental developments in designing efficient IS for practical use. This includes parametric approximation with optimization based adaptation, sequential sampl...

In this paper, we propose a nonuniform DFT based on nonequispaced sampling in the frequency domain. It is useful to detect some specific frequencies such as in DTMF which is composed on two different frequencies, main (fundamental) frequency component among lots of harmonics, and feature detection from noisy signals. Some trials for nonuniform data processing via DFT are discussed and resamplin...

Monte Carlo estimation of the power of tests that require resampling can be very com-putationally intensive. It is possible to reduce the size of the inner resampling loop as long as the resulting estimator of power can be corrected for bias. A simple linear extrapolation method is shown to perform well in correcting for bias and thus reduces computation time in Monte Carlo power studies.

Re-sampling methods have long been used in survey sampling, dating back to Mahalanobis (1946). More recently, jackknife and bootstrap resampling methods have also been proposed for small area estimation; in particular for mean squared error (MSE) estimation and for constructing confidence intervals. We present a brief overview of early uses of resampling methods in survey sampling, and then pro...

A new pseudo-noise resampling technique is proposed to mitigate the effect of outliers in Unitary ESPRIT. This scheme improves the performance of Unitary ESPRIT in unreliable situations, where the so-called reliability test has a failure. For this purpose, we exploit a pseudo-noise resampling of a failed Unitary ESPRIT estimator with a censored selection of “successful” resamplings recovering t...

Sparked by Efron’s seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data— mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random fields. Consequently, the availability of valid nonparametric inference procedures based on resampling ...

In the development of neural networks, many realizations are performed to decide which solution provides smallest prediction error. Due inevitable random errors associated with data and randomness related network (e.g., initialization weight initial conditions linked learning procedure), there is usually not an optimal solution. However, we can advantage idea making several based on resampling ...

We use resampling techniques to analyze tile impact of provklers on workers' compensation costs taking into consideration inherent differences in claim populations between providers. Resampling techniques provide a nonparametric determination of a statistic's distribution and a measure of effectiveness that is not sensitive to deviations from the assumptions underlying most parametric statistic...

A standard method for visualizing vector elds consists of drawing many small \glyphs" to represent the eld. This paper extends the technique from regular to curvilinear and unstructured grids. In order to achieve a uniform density of vector glyphs on nonuniformly spaced grids, the paper describes two approaches to resampling the grid data. One of the methods, an element-based resampling, can be...

We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous-path block bootstrap scheme applied to a full rank integrated process, succeeds in estimating consistently the distribution of the least squares estimators in both, the regression and the spurious regressio...