| preface 1 introduction 2 parametric empirical methods 2.1 auxiliary results 2.2 models with independent observations 2.3 models with continuous time 2.4 models with restrictions in the form of inequalities 2.5 nonstationary empirical estimates 3 parametric regression models 3.1 estimates of the parameters for gaussian regression mod-els with discrete time 3.2 estimates of the parameters for gaussian random field with a continuous argument 3.3 nonstationary regression model for gaussian field 3.4 identification of the parameters for the stationary nonlin-ear regression as a special case of stochastic programming problem 3.5 nonstationary regression model for a random field ob-served in a circle 3.6 gaussian regression models for quasistationary randomprocesses 4 periodogram estimates for random processes and fields 4.1 preliminary results 4.2 asymptotic behavior of periodogram estimates of the first type 4.3 asymptotic behavior of periodogram estimates of the second type 4.4 periodogram estimates in rm .5 nonparametric identification problems 5.1 the investigation of the general problem 5.2 the nonparametric regression model with observations in a finite number of curves on the plane 5.3 the nonparametric regression model with observations in nodes of a rectangle 5.4 the periodical signal estimation by observation of its mix- ture with homogeneous random field references |
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