Slotted resampling transforms an irregularly sampled process into an equidistantly sampled signal where data are missing. Equidistant resampling always causes spectral bias, due to aliasing and to shifting of the observation times. The shift bias can be diminished by using a slot width that is smaller than the resampling time step. A special approximate maximum likelihood time series estimator ...

Bagging is a simple way to combine estimates in order to improve their performance. This method, suggested by Breiman in 1996, proceeds by resampling from the original data set, constructing a predictor from each subsample, and decide by combining. By bagging an n-sample, the crude nearest neighbor regression estimate is turned into a consistent weighted nearest neighbor regression estimate, wh...

IMPORTANCE RESAMPLING FOR GLOBAL ILLUMINATION Justin F. Talbot Department of Computer Science Master of Science This thesis develops a generalized form of Monte Carlo integration called Resampled Importance Sampling. It is based on the importance resampling sample generation technique. Resampled Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration...

Slotted resampling transforms an irregularly sampled process into an equidistant missing-data problem. Equidistant resampling inevitably causes bias, due to aliasing and the shift of the irregular observation times to an equidistant grid. Taking a slot width smaller than the resampling time can diminish the shift bias. A dedicated estimator for time series models of multiple slotted data sets w...

To solve the problem of particle degeneracy and sample impoverishment in conventional particle filter, we propose the weight approaching particle filter(WAPF) to increase the particle diversity before resampling step for adaptive multi-user detection (MUD) in synchronous code division multiple access (CDMA) system.. In the resampling step, particles are classified into two groups according to t...

SUMMARY Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstr...

Many machine learning models have important structural tuning parameters that cannot be directly estimated from the data. The common tactic for setting these parameters is to use resampling methods, such as cross–validation or the bootstrap, to evaluate a candidate set of values and choose the best based on some pre–defined criterion. Unfortunately, this process can be time consuming. However, ...

The problem of digital signal and image resampling with discrete sinc interpolation is addressed. Discrete sine interpolation is theoretically the best one among the digital convolution-based signal resampling methods because it does not distort the signal as defined by its samples and is completely reversible. However, sinc interpolation is frequently not considered in applications because it ...