Stokastik difrensiyel denklemler için çeşitli parametre tahmin yöntemleri

Dynamic systems appear in many fields from economics to physics, from biology toengineering include randomness. Therefore, stochastic differential equations are oneof the necessary mathematical tools to model dynamic systems in these disciplines.In this study, we propose two parameter estimation methods when modelling withSDEs which are driven by Brownian motion. Maximum likelihood estimation andgeneralized method of moment techniques are used to estimate parameters and it isobtained that when the assumptions for Brownian motion satisfy, both techniques givethe same result.Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Statistics