Journal of Nonparametric Statistics 

About: Journal of Nonparametric Statistics is an academic journal. The journal publishes majorly in the area(s): Estimator & Nonparametric statistics. It has an ISSN identifier of 1026-7654. Over the lifetime, 1273 publication(s) have been published receiving 17790 citation(s).

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.

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

Testing for dependence in the input to a linear time series model

Abstract: This paper presents a simple test for dependence in the residuals of a linear parametric time series model fitted to non gaussian data. The test statistic is a third order extension of the standard correlation test for whiteness. but the number of lags used in this test is a function of the sample size. The power of this test goes to one as the sample size goes to infinity for any alternative which has non zero bicovariances c e3(r,s)= E[e(t)e(t + r)e(t + s)] for a zero mean stationary random time series. The asymptotic properties of the test statistic are rigorously determined. This test is important for the validation of the sampling properties of the parameter estimates for standard finite parameter linear models when the unobserved input (innovations) process is white but not gaussian. The sizes and power derived from the asymptotic results are checked using artificial data for a number of sample sizes. Theoretical and simulation results presented in this paper support the proposition that the test wi...

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.

A new class of random vector entropy estimators and its applications in testing statistical hypotheses

Abstract: This paper proposes a new class of estimators of an unknown entropy of random vector Its asymptotic unbiasedness and consistency are proved Further, this class of estimators is used to build both goodness-of-fit and independence tests based on sample entropy A simulation study indicates that the test involving the proposed entropy estimate has higher power than other well-known competitors under heavy tailed alternatives which are frequently used in many financial applications