Choosing appropriate statistical tests is crucial but deciding which to use can be challenging. Different suit different types of data and research questions, so it important choose the right one. Knowing how select an test lead more accurate results. Invalid results misleading conclusions may drawn from a study if incorrect used. Therefore, avoid these essential understand nature data, questio...

Synthetic aperture radar (SAR) has a pivotal role as a remote imaging method. Obtained by means of coherent illumination, SAR images are contaminated with speckle noise. The statistical modelling of such contamination is well described according the multiplicative model and its implied G distribution. The understanding of SAR imagery and scene element identification is an important objective in...

We propose two new types of nonparametric tests for investigating multivariate regression functions. The tests are based on cumulative sums coupled with either minimum volume sets or inverse regression ideas; involving no multivariate nonparametric regression estimation. The methods proposed facilitate the investigation for different features such as if a multivariate regression function is (i)...

Nonparametric tests for the null hypothesis that a function has a prescribed form are developed and applied to data sets with missing observations. Omnibus nonparametric tests such as the order selection tests, do not need to specify a particular alternative parametric form, and have power against a large range of alternatives. More specifically, likelihood-based order selection tests are defin...

In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the res...

Correlation among multiple complex traits has been believed to have a significant impact on genetic association testing. However, such correlation is difficult to account for in both genetic association studies and statistical methodologies. For complex diseases, especially mental illnesses and behavioral disorders, the traits are often measured on an ordinal scale. Although proportional odds l...

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...

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman-Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the ...

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...