Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for ...

Including a method factor on which all items loaded significantly improved fit of CFAs of both IPIP and NEO-FFI data. Fit was further improved by separating the method factor into two factors representing construct-positive and construct-negative items. The criterion-related validity of conscientiousness increased when uncontaminated by method bias. PRESS PARAGRAPH This research extends previou...

Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric...

In order to determine the normal direction of the magnetopause, the minimum variance analysis technique is frequently used: it is applied to the magnetic field data of a magnetopause crossing observed by a satellite, and provides the direction along which the magnetic field variation is minimum. In this study we propose a method to extend naturally the framework of the minimum variance analysis...

The implicit Monte Carlo (IMC) method is an important numerical approximation of simulating the thermal radiative transfer problems under high temperature condition. However, one problem plaguing IMC that calculation error distributions radiation specific intensities are highly asymmetric in space and time. By theoretical analysis simulations, we find affected by records track tallying mesh. Ac...