Journal of Nonparametric Statistics 

About: Journal of Nonparametric Statistics is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Estimator & Nonparametric statistics. It has an ISSN identifier of 1026-7654. Over the lifetime, 1342 publications have been published receiving 19962 citations.

Plug-in bandwidth matrices for bivariate kernel density estimation

Abstract: We consider bandwidth matrix selection for bivariate kernel density estimators. The majority of work in this area has been directed towards selection of diagonal bandwidth matrices, but full bandwidth matrices can give markedly better performance for some types of target density. Our methodological contribution has been to develop a new version of the plug-in selector for full bandwidth matrices. Our approach has the advantage, in comparison to existing full bandwidth matrix plug-in techniques, that it will always produce a finite bandwidth matrix. Furthermore, it requires computation of significantly fewer pilot bandwidths. Numerical studies indicate that the performance of our bandwidth selector is best when implemented with two pilot estimation stages and applied to sphered data. In this case our methodology performs at least as well as any competing method considered, while being simpler to implement than its competitors.

Multiple predicting K-fold cross-validation for model selection

Abstract: K-fold cross-validation (CV) is widely adopted as a model selection criterion. In K-fold CV, folds are used for model construction and the hold-out fold is allocated to model validation. This impli...

A K-fold Averaging Cross-validation Procedure

Abstract: Cross-validation (CV) type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold CV procedure to select a candidate ‘optimal’...

Distribution-free multiple comparisons between successive treatments

Abstract: Distribution-free methods are proposed for one- and two-sided comparison of adjacent pairs of r≧3 ordered treatments in the one-way layout. A table of exact probability points isgiven for use with Mann Whitney statistics in the equal sample size case for one-sided inference with r = 3 or 4 and for two-sided inference with r = 3. Approximations are investigated for use with larger r. Asymptotic probability points are found for use with all Chernoff-Savage statistics. Asymptotically optimal designs for these methods use approximately equal numbers of observations for even r and approximately ((r+l)/(r-1))1/2 times as many observations from each even numbered treatment as from each odd numbered treatment when r is odd. The asymptotic relative efficiency oftwo proceduresis identical to that of their two-sample counterparts

Density estimation using inverse and reciprocal inverse Gaussian kernels

Abstract: This paper introduces two new nonparametric estimators for probability density functions which have support on the non-negative half line. These kernel estimators are based on some inverse Gaussian and reciprocal inverse Gaussian probability density functions used as kernels. We show that they share the same properties as those of gamma kernel estimators : they are free of boundary bias, always non-negative, and achieve the optimal rate of convergence for the mean integrated squared error. Extensions to regression curve estimation and hazard rate estimation under random censoring are briefly discussed. Monte Carlo results concerning finite sample properties are reported for different distributions.