This paper considers a class of nonparametric autoregressive processes and then a class of nonparametric time series regression models with a nonstationary regressor. For the autoregression case, we propose a nonparametric unit–root test for the conditional mean. For the nonparametric time series regression case, we construct a nonparametric test for testing whether the regression is of a known...

Generally, multiple statistical analysis methods can be applied for certain kind of data, and conclusion could differ, depending on the selected statistical method. Therefore, it is necessary to fully understand the performance of each statistical method and to examine which method is appropriate to use and to standardize statistical methods for toxicity studies to be carried out routinely. Sev...

This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test.

This paper considers a nonparametric time series regression model with a nonstationary regressor. We construct a nonparametric test for testing whether the regression is of a known parametric form indexed by a vector of unknown parameters. We establish the asymptotic distribution of the proposed test statistic. Both the setting and the results differ from earlier work on nonparametric time seri...

In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals from a parametric regression on some covariates. It is close to 0 under the null hypothesis of c...

d t p d t A d n HE CHOICE OF STATISTICAL TEST HAS A PROFOUND impact on the interpretation of data. Understanding this choice is important for the critical evaluation of he biomedical literature. The question often arises hether to use nonparametric or parametric tests. The test is the most widely used statistical test for comparing he means of 2 independent groups. This parametric test ssumes t...