On statistical estimation and inferences in optional regression models

The main object of investigation in this paper is a very general regression model optional setting - when an observed process semimartingale depending on unknown parameter. It well-known that statistical data may present information flow/filtration without usual conditions. estimation problem achieved by means structural least squares (LS) estimates and their sequential versions. results the are devoted to strong consistency such LS-estimates. For LS-estimates property fixed accuracy proved.