The Behrens-Fisher Problem, an Old Solution Revisited.
The BERRY-ESSEEN Bound for Minimum Contrast Estimates
The Berry-Esseen Bound for Minimum Contrast Estimators in the Independent not Identically Distributed Case.
The Berry-Esséen Theorem for the Subject-Years Method in Mortality Analysis with Censored Data.
The bootstrap of the mean with arbitrary bootstrap sample size
The Bound in the Berry-Esseen Result for Minimum Contrast Estimates.
The Central Limit Theorem for Maximum Likelihood Estimators of Vector Parameters: Locally Uniform Convergence.
The Conditional Maximum Likelihood Estimator of the Shape Parameter in the Gamma Distribution.
The Density of the Parameter Estimators when the Observations are Distributed Exponentially.
The effect of a single point on correlation and slope.
The effect of loss functions on empirical Bayes reliability analysis.
The efficiency of estimates in stationary autoregressive series
The efficiency of modified jackknife and ridge type regression estimators: a comparison.
The generalized p-point method of estimation of regression coefficients
The importance of being the upper bound in the bivariate family.
Any bivariate cdf is bounded by the Fréchet-Hoeffding lower and upper bounds. We illustrate the importance of the upper bound in several ways. Any bivariate distribution can be written in terms of this bound, which is implicit in logit analysis and the Lorenz curve, and can be used in goodness-of-fit assesment. Any random variable can be expanded in terms of some functions related to this bound. The Bayes approach in comparing two proportions can be presented as the problem of choosing a parametric...
The infinitesimal robustness of tests against dependence
The integrated squared error estimation of parameters.
This paper deals with the problem of estimation in the parametric case for discrete random variables. Their study is facilitated by the powerful method of probability generating function.
The least trimmed squares. Part I: Consistency
The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al. [HamRonRouSta]) is proved under general conditions. The assumptions employed in paper are discussed in details to clarify the consequences for the applications.
The least trimmed squares. Part II: -consistency
-consistency of the least trimmed squares estimator is proved under general conditions. The proof is based on deriving the asymptotic linearity of normal equations.
The least trimmed squares. Part III: Asymptotic normality
Asymptotic normality of the least trimmed squares estimator is proved under general conditions. At the end of paper a discussion of applicability of the estimator (including the discussion of algorithm for its evaluation) is offered.