Monte Carlo simulation methods apply a random sampling and modifications can be made of this method is by using variance reduction techniques (VRT). VRT objective is to reduce the variance due to Monte Carlo methods become more accurate with a variance approaching zero and the number of samples approaches infinity, which is not practical in the real situation (Chen, 2004). These techniques are ...

Efficiency-Improvement Techniques Ref: Law and Kelton, Chapter 11 1. Variance Reduction and Efficiency Improvement The techniques that we will look at typically are designed to reduce the variance of the estimator for the performance measure that we are trying to estimate via simulation. (Reduction of the variance leads to narrower confidence intervals, and hence less computational effort is re...

One way of handling survey nonresponse is to impute data for each nonrespondent. When estimating sampling variances, however, treating the imputed data as a complete set frequently leads to underestimates of the true sampling variance. Techniques have been recently developed to yield valid variance estimates in the presence of imputed data for some estimators and sample designs. Economic survey...

Variances in the American Community Survey are estimated using a replicate weight methodology (Fay, 1995). In counties with small sample sizes, the variance estimates of poverty statistics show wide variation as a function of sample size. Generalized Variance Functions (GVF) can be used to smooth out the uncertainty of the design-based variance estimate. We propose incorporating GVFs with small...

The problem of how to estimate variance parameters in client models from scarce data is addressed in the context of textdependent, HMM-based, automatic speaker verification. Variance flooring and variance scaling are investigated as two alternative estimation techniques and are used with or without variance tying on the state level to reduce the number of parameters to estimate. The best result...