On variance-covariance components estimation in linear models with AR(1) disturbances.
On various criteria of optimality in probabilistic decision-making
On Weak Convergence of Parameter Estimators of General Statistical Parametric Models
On weak convergence of sequences of measures
On Weak Convergence Of Spectral Density Estimate
On Weighted Parametric Information Measure
On χ² tests of composite hypotheses
One Bootstrap suffices to generate sharp uniform bounds in functional estimation
We consider, in the framework of multidimensional observations, nonparametric functional estimators, which include, as special cases, the Akaike–Parzen–Rosenblatt kernel density estimators ([1, 18, 20]), and the Nadaraya–Watson kernel regression estimators ([16, 22]). We evaluate the sup-norm, over a given set , of the difference between the estimator and a non-random functional centering factor (which reduces to the estimator mean for kernel density estimation). We show that, under suitable general...
One Case of Reduction of Nonlinear Regression to Linear
One multivariate linear model with nuisance parameters
One of the calibration problems
One prediction of autoregressive time series
One sample tests for the location of modes of nonnormal data.
One singular multivariate linear model with nuisance parameters
The multivariate linear model, in which the matrix of the first order parameters is divided into two matrices: to the matrix of the useful parameters and to the matrix of the nuisance parameters, is considered.
One-Sided and Multi-Sided Rank Tests Against Trend in the Case of Concordant Observers.
One-sided approximation of Bayes rule and its application to regression model with Cauchy noise
On-line nonparametric estimation.
A survey of some recent results on nonparametric on-line estimation is presented. The first result deals with an on-line estimation for a smooth signal S(t) in the classic 'signal plus Gaussian white noise' model. Then an analogous on-line estimator for the regression estimation problem with equidistant design is described and justified. Finally some preliminary results related to the on-line estimation for the diffusion observed process are described.
Operator semigroups and Poisson convergence in selected metrics.
Optimal alternative robustness in Bayesian Decision Theory.
In Martin et al (2003), we suggested an approach to general robustness studies in Bayesian Decision Theory and Inference, based on ε-contamination neighborhoods. In this note, we generalise the results considering neighborhoods based on norms, specifically, the supremum norm for utilities and the total variation norm for probability distributions. We provide tools to detect changes in preferences between alternatives under perturbations of the prior and/or the utility and the most sensitive direction....
Optimal and Robust Invariant Designs for Cubic Multiple Regression.