To test heteroscedasticity in single index models, in this paper two test statistics are proposed via quadratic conditional moments. Without the use of dimension reduction structure, the first test has the usual convergence rate in nonparametric sense. Under the dimension reduction structure of...

For a p-dimensional normal distribution with mean vector [theta] and covariance matrix Ip, it is known that the maximum likelihood estimator [theta] of [theta] with p[greater-or-equal, slanted]3 is inadmissible under the squared loss. The present paper considers possible extensions of the result...

This paper deals with the improved forecasts for the values of the study variable in linear regression models utilizing the minimum risk approach. It considers the simultaneous forecasting of actual and average values of the study variable and reports the performance properties of the classical...

Estimating the density function of a random vector taking values on the d-dimensional unit sphere is considered. Also the estimation of the Laplacian of the density and estimation of other types of derivatives is considered. Fast convergence rate theory is developed for pointwise, L1, and L2...

In order to construct confidence sets for a marginal density f of a strictly stationary continuous time process observed over the time interval [0, T], it is necessary to have at one's disposal a Central Limit Theorem for the kernel density estimator fT. In this paper we address the question...

We consider a nonparametric regression model where the response Y and the covariate X are both functional (i.e. valued in some infinite-dimensional space). We define a kernel type estimator of the regression operator and we first establish its pointwise asymptotic normality. The double...

The paper proposes new procedures for diagnostic checking of fitted models under the assumption of infinite-variance errors which are in the domain of attraction of a stable law. These procedures are functional of residual-based empirical processes. First, the asymptotic distributions of the...

We give a complete description of the rate of strong consistency of the scaled and unscaled total time on test curves, which are fundamental notions in the statistical theory of reliability and life testing. The proof is crucially based on the general Vervaat process.

It is well known that, asymptotically, the appropriately normalized Vervaat process behaves like one half times the squared empirical process. Considering these two processes as elements of the Lp-space, 1[less-than-or-equals, slant]p[infinity], we give a complete description of the strong and...

The paper presents a permutation procedure for testing reflected (or diagonal) symmetry of the distribution of a multivariate variable. The test statistics are based in empirical characteristic functions. The resulting permutation tests are strictly distribution free under the null hypothesis...