We propose a nonparametric procedure to test the hypothesis that the j-th largest eigenvalues of a covariance matrix are equal in multipopulation. We apply the Mood test by using the principal component scores and deal the equality of eigenvalues with the equality of variance. We investigate the significance level and the power of test by simulation and show that this nonparametric test is usef...

Iowa State University, The University of Western Australia and Iowa State University We propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process. The empirical likelihood is used to formulate a statistic, for each kernel smoothing bandwidth, which is effectively a studentized L2-distance between the kernel t...

Consider two random variables contaminated by two unknown transformations. The aim of this paper is to test the equality of those transformations. Two cases are distinguished: first, the two random variables have known distributions. Second, they are unknown but observed before contaminations. We propose a nonparametric test statistic based on empirical cumulative distribution functions. Monte ...

In this paper we suggest a completely nonparametric test for the assessment of similar marginals of a multivariate distribution function. This test is based on the asymptotic normality of Mallows distance between marginals. It is also shown that the n out of n bootstrap is weakly consistent, thus providing a theoretical justification to the work in Czado & Munk [12]. The test is extended to cro...

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 statistical pattern recognition, class conditional probability distribution is estimated and used for classification. Since it is impossible to estimate the true distribution, usually the distribution is assumed to be a certain parametric model like normal distribution and the parameters that represent the distribution are estimated from training data. However there is no guarantee that the ...

Viral replication is often analyzed by growth curves, in which viral multiplication in the presence of host cells is measured as a function of time. Comparing growth curves is one of the most sensitive ways of comparing viral growth under different conditions or for comparing replication of different viral mutants. However, such experiments are rarely analyzed in a statistically rigorous fashio...

Studies based on casual blood pressure measurements concluded that both age and parity have significant effects on blood pressure during pregnancy. We have tested these results on clinically healthy normotensive women who were systematically studied by ambulatory blood pressure monitoring during their pregnancies. We analyzed 1254 blood pressure series sampled for 48 consecutive hours every 4 w...

Despite an abundance of semiparametric estimators of the transformation model, no procedure has been proposed yet to test the hypothesis that the transformation function belongs to a finite-dimensional parametric family against a nonparametric alternative. In this paper we introduce a bootstrap test based on integrated squared distance between a nonparametric estimator and a parametric null. As...