A parametric bootstrap test for cycles

Parametric Bootstrap for Test of Contrast Difference in Neural Networks

This work concernes the contrast difference test and its asymptotic properties for non linear auto-regressive models. Our approach is based on an application of the parametric bootstrap method. It is a re-sampling method based on the estimate parameters of the models. The resulting methodology is illustrated by simulations of multilayer perceptron models, and an asymptotic justification is give...

A Parametric Bootstrap Version of Hedges’ Homogeneity Test

Comparison of parametric and bootstrap method in bioequivalence test.

The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 a...

A Bootstrap Test for Time Series Linearity

A bootstrap algorithm is proposed for testing Gaussianity and linearity in stationary time series, and consistency of the relevant bootstrap approximations is proven rigorously for the first time. Subba Rao and Gabr (1980) and Hinich (1982) have formulated some well-known nonparametric tests for Gaussianity and linearity based on the asymptotic distribution of the normalized bispectrum. The pro...

A bootstrap test for equality of variances

We introduce a bootstrap procedure to test the hypothesis Ho that K + 1 variances are homogeneous. The procedure uses a variance-based statistic, and is derived from a normal-theory test for equality of variances. The test equivalently expressed the hypothesis as Ho : η = (η1, . . . , ηK+1) = 0, where ηi’s are log contrasts of the population variances. A box-type acceptance region is constructe...