Silvelyn Zwanzig

Learn More
On the Comparison of parametric and nonparametric bootstrap. Abstract This paper discusses the performance of parametric and nonparametric bootstrap. We show that the performance of parametric and nonparametric bootstrap for variance estimation depends on the sample kurtosis and on the kurtosis of the distribution used to generate the bootstrap observations(More)
In this paper, we propose a new test for examining the equality of the coefficient of variation between two different populations. The proposed test is based on the nonparametric bootstrap method. It appears to yield several appreciable advantages over the current tests. The quick and easy implementation of the test can be considered as advantages of the(More)
1 Local linear methods are applied to a nonparametric regression model with normal errors in the variables and uniform distribution of the variables. The local neighborhood is determined with help of decon-volution kernels. Two different linear estimation method are used: the naive estimator and the total least squares estimator. Both local linear(More)
Resampling and simulation methods like bootstrap or Simex are based on new generated observations. The name simulation experiment is introduced for the family of probability measures of these simulated samples. The family of measures of the original sample is called original experiment. Both types of experiments are defined on different probability(More)
Local linear regression methods are applied to a nonparametric errors-in-variables model with normal errors in the variables and uniform distribution of the variables. The local neighborhood is determined with help of deconvolution kernels. Two different linear estimation method are used: the naive estimator and the total least squares estimator. Both local(More)
  • 1