MOTIVATION A primary objective of microarray studies is to determine genes which are differentially expressed under various conditions. Parametric tests, such as two-sample t-tests, may be used to identify differentially expressed genes, but they require some assumptions that are not realistic for many practical problems. Non-parametric tests, such as empirical Bayes methods and mixture normal ...

Testing for the randomness of a time series has been one of the most widely researched topics in time-series analysis. The present paper carries out a comparative study of the &nite-sample performance of some well-known portmanteau tests in this area. Using Monte Carlo simulation experiments, we &nd that (i) the empirical sizes of some oft-used parametric portmanteau tests are severely undersiz...

We propose a nonparametric likelihood ratio testing procedure for choosing between a parametric (likelihood) model and a moment condition model when both models could be misspecified. Our procedure is based on comparing the Kullback–Leibler Information Criterion (KLIC) between the parametric model and moment condition model. We construct the KLIC for the parametric model using the difference be...

In this work, we get focused on the use of statistical techniques for behavior analysis of Artificial Neural Networks in the task of classification. A study of the non-parametric tests use is presented, using some well-known models of neural networks. The results show the need of using non-parametric statistic, because the Artificial Neural Networks used do not verify the hypothesis required fo...

1. Introduction In conventional setup the analysis of experimental data is based on the assumptions like normality, independence and constant variance of the observations. However, there may arise experimental situations where these assumptions, particularly the assumption of normality, may not be satisfied. In such situations non-parametric test procedures may become quite useful. A lot of att...

Goodness-of-fit tests are a fundamental element in the copula-based modeling of multivariate continuous distributions. Among the different procedures proposed in the literature, recent large scale simulations suggest that one of the most powerful tests is based on the empirical process comparing the empirical copula with a parametric estimate of the copula derived under the null hypothesis. As ...

Rotationally symmetric distributions on the unit hyperpshere are among the most successful ones in directional statistics. These distributions involve a finite-dimensional parameter θ and an infinite-dimensional parameter g1, that play the role of “location” and “angular density” parameters, respectively. In this paper, we consider semiparametric inference on θ, under unspecified g1. More preci...

abstract the aim of this paper is to investigate annual and seasonal precipitation trends of some selective meteorological stations of central region in iran using non-parametric methods. two non-parametric methods including mann-kendall and sen's estimator slope methods were used here to analyze the seasonal and annual trends of precipitation data. some 48 meteorological stations in centr...