Synthetic aperture magnetometry (SAM) is a nonlinear beamformer technique for producing 3D images of cortical activity from magnetoencephalography data. We have previously shown how SAM images can be spatially normalised and averaged to form a group image. In this paper we show how nonparametric permutation methods can be used to make robust statistical inference about group SAM data. Data from...

Methods of estimation of density and regression function are quite common in statistical applications. Wavelet theory has the potential to provide statisticians with powerful new techniques for nonparametric inference. It combines recent advances in approximation theory with insights gained from applied signal analysis. Nonparametric curve estimation by wavelets has been treated in numerous art...

Nonparametric additive modeling is a fundamental tool for statistical data analysis which allows flexible functional forms for conditional mean or quantile functions but avoids the curse of dimensionality for fully nonparametric methods induced by high-dimensional covariates. This paper proposes empirical likelihood-based inference methods for unknown functions in three types of nonparametric a...

In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons...

difference aspects of multinomial statistical modelings and its classifications has been studied so far. in these type of problems y is the qualitative random variable with t possible states which are considered as classifications. the goal is prediction of y based on a random vector x ? ir^m. many methods for analyzing these problems were considered. one of the modern and general method of cla...