Adaptive efficient estimation for generalized semi‑Markov big data models Process 22:187-231, 2019
a). For such models we consider
estimation problems in
nonparametric setting
Дисперсионные характеристики статических моделей стохастических объектов concerning optimized parameters. It is shown that the
nonparametric estimation of
regression is
close
Robust semiparametric and semi-nonparametric estimates of inhomogeneous experimental dataA weighted maximum likelihood method (WMLM) of robust
estimation of experimental data with outliers
Using robust regression methods for improve the accuracy of the estimating of observational models parameters the ordinary least squares
estimation method (LSE, OLS),
a computational scheme of RA, and its generalizations
Adaptive algorithm of classifcation on the missing data. Computational procedures are based on
non-parametric estimation, are given their settings and the results
Comparative analysis of methods for simulating the well operation with electric submersible pump installations of the Rosenblatt–Parzen
non-parametric regression, parametric models with automatic adaptation of parameters
On Non-parametric Models of Multidimensional Non-inertial Processes with Dependent input Variables. Components of the input vector are stochastically related, and this relationship is unknown
a priori
Comparative analysis of methods for simulating the well operation with electric submersible pump installations constructed with the help of the Rosenblatt-Parzen
non-parametric regression, parametric models with automatic