A general multivariate central limit theorem of M. G. Hahn, P. Hahn, and M. J. Klass (1983, Ann. Probab. 11 277-301) gives conditions relating the one-dimensional marginals of the summands in an infinitesimal triangular array to those of the limit law. This theorem is applied here to the sample...

This paper addresses the problem of estimating the population coefficient of agreement kappa (?) among a set of raters who independently classify a randomly selected subject into one of two categories. Of the many possible probability models for these classifications, only mixtures of binomial...

This paper considers the problems of estimating a mean vector [mu] under constraint [mu]'[Sigma]-1[mu] = 1 or [Sigma]-1/2[mu] = c and derives the best equivariant estimators under the loss (a - [mu])' [Sigma]-1(a - [mu]), which dominate the MLE's uniformly. The results are regarded as...

General convergence results for maximum likelihood type estimators in multivariable ARMA-models under very weak assumptions are given. This extends results by Dunsmuir and Hannan (1976, Advan. Appl. Probab. 8 339-364) and Deistler, Dunsmuir, and Hannan (1978, Advan. Appl. Probab. 10 360-372). In...

Generalizing a result of [4], for location parameter families, it was shown by [11], that Pitman estimators are minimax for translation invariant experiments with a Euclidean parameter space. In the present paper this theorem is extended to experiments whose parameter space is homogeneous under...

We consider the kernel estimation of a multivariate regression function at a point. Theoretical choices of the bandwidth are possible for attaining minimum mean squared error or for local scaling, in the sense of asymptotic distribution. However, these choices are not available in practice. We...

Multiple linear regression models with non random regressors in continuous time are considered. The strong consistency of least squares estimates is established under minimal assumptions on the design when the process of errors is a semimartingale satisfying some regularity condition.

Simple formulas are proved for the dimensions of vector spaces of homogeneous zero regression polynomials. A simplified characterization of these polynomials is proved.

Let [Theta] be a real random variable with a known (a priori) distribution. Let the observations given [Theta] be iid with a common distributionF0(·)=F(·-0)F is known to belong to some family of distributions on the real line. We find estimators of [Theta] which are asymptotically minimax...