In breeding for plant disease resistance programs, a large number of new improved genotypes are tested over a range of test pathogens or environments and the underlying statistics used to model this system may be rather complicated. Due to ordinal nature of most measured traits of disease responses, some nonparametric methods used for analyzing genotype × environment (GE) interaction in two dat...

The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. Fo...

The receiver operating characteristic (ROC) curve methodology is the statistical methodology for assessment of the accuracy of diagnostics tests or bio-markers. Currently most widely used statistical methods for the inferences of ROC curves are complete-data based parametric, semi-parametric or nonparametric methods. However, these methods cannot be used in diagnostic applications with missing ...

ABSTRACf Most of the other chapters in this volume are devoted to various parametric models arising in the classical sequential analysis pertaining to statistical decision theory including sequential testing as well as {point and interval} estimation problems. Though of relatively recent origins, various nonparametric models have also been worked out for these tradi tional problems in sequentia...

The aim of this paper is to show how teachers can use Visual Basic language on the spreadsheet for student sin order to gain the skills need in using nonparametric techniques for density and regression function estimations. The author has built a useful didactic tool on Excel Visual Basic for teaching and practice of nonparametric kernel methods, being intended for students with some preliminar...

We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive a method for fitting the models that is effective even when the number of covariates is larger than the sample size. A statistical analysis of the properties ...