Sufficient conditions are given to ensure a better performance of the plug-in version of the covariates adjusted location linear discriminant function in an asymptotic comparison of the overall expected error rate. Our findings generalize several earlier results on discriminant function with...

The distribution of the sum of independent nonidentically distributed Bernoulli random vectors inRkis approximated by a multivariate Poisson distribution. By using a multivariate adaption of Kerstan's (1964,Z. Wahrsch. verw. Gebiete2, 173-179) method, we prove a conjecture of Barbour (1988,J....

The asymptotic distribution of the sample canonical correlations and coefficients of the canonical variates is obtained when the nonzero population canonical correlations are distinct and sampling is from the normal distribution. The asymptotic distributions are also obtained for reduced rank...

We prove an almost sure central limit theorem for some multidimensional stochastic algorithms used for the search of zeros of a function and known to satisfy a central limit theorem. The almost sure version of the central limit theorem requires either a logarithmic empirical mean (in the same...

For a multinormal distribution with an unknown dispersion matrix, union-intersection (UI) tests for the mean against one-sided alternatives are considered. The null distribution of the UI test statistic is derived and its power monotonicity properties are studied. A Stain-type two-stage...

K. I. Yoshihara (1990,Comput. Math. Appl.19, No. 1, 149-158) proved the weak invariance of the conditional nearest neighbor regression function estimator called the conditional empirical process based on[phi]-mixing observations. In this paper, we extend the result for nonstationary and...

In this paper exact formulae of the probability density function for the spherically symmetric distribution with marginal logistic are given. They are entirely different for odd and even dimensions. For an odd number of dimensions it is possible to express them by elementary functions but for an...

The minimum covariance determinant (MCD) scatter estimator is a highly robust estimator for the dispersion matrix of a multivariate, elliptically symmetric distribution. It is relatively fast to compute and intuitively appealing. In this note we derive its influence function and compute the...

The problem of nonparametric estimation of a multivariate density function is addressed. In particular, a general class of estimators with favorable asymptotic performance (bias, variance, rate of convergence) is proposed. The proposed estimators are characterized by the flatness near the origin...

In this paper, we build a central limit theorem for triangular arrays of sequences which satisfy a mild mixing condition. This result allows us to study asymptotic normality of density kernel estimators for some classes of continuous and discrete time processes.