This paper studies a semi-linear errors-in-variables model of the formYi=x'i[beta]+g(Ti)+ei,Xi=xi+ui(1[less-than-or-equals, slant]i[less-than-or-equals, slant]n). The estimators of parameters[beta],[sigma]2and of the smooth functiongare derived by using the nearest neighbor-generalized least...

To analyze the isotonic regression problem for normal means, it is usual to assume that all variances are known or unknown but equal. This paper then studies this problem in the case that there are no conditions imposed on the variances. Suppose that we have data drawn fromkindependent normal...

We consider a test for spherical symmetry of a distribution in dwith an unknown center. It is a multivariate version of the tests suggested by Schuster and Barker and by Arcones and Giné. The test statistic is based on the multivariate extension of the distribution and quantile functions,...

The relative motion between two diverging tectonic plates is a rotation of the sphere. Given measurements of points on the boundaries of the plates, the rotation can be estimated by minimizing a function which is asymptotically (as the concentration parameter of the data distribution goes to...

Motivated by problems of frontier estimation in productivity analysis, and boundary estimation in scatter-point image analysis, we consider polynomial-based estimators of the edge of a distribution. Our aim is to develop methods for correcting polynomial-type estimators of bias, and for...

The problem of estimating the mean of a multivariate normal distribution is considered. A class of admissible minimax estimators is constructed. This class includes two well-known classes of estimators, Strawderman's and Alam's. Further, this class is much broader than theirs.

In this paper we show how discrete and continuous variables can be combined using parametric conditional families of distributions and how the likelihood weighting method can be used for propagating uncertainty through the network in an efficient manner. To illustrate the method we use, as an...

It is well known that a bivariate distribution belongs to the domain of attraction of an extreme value distribution G if and only if the marginals belong to the domain of attraction of the univariate marginal extreme value distributions and the dependence function converges to the stable tail...

By use of cyclic subspaces, explicit connections between principal component regression (PCR) and partial least squares (PLS) are established that shed light onto why one method works better than the other. These connections clearly identify how both methods make use of calibration data in...

This paper presents a generalization of Rao's covariance structure. In a general linear regression model, we classify the error covariance structure into several categories and investigate the efficiency of the ordinary least squares estimator (OLSE) relative to the Gauss-Markov estimator (GME)....