Statistical inference for second order ordinary differential equation driven by additive Gaussian white noise

2018-07-26This thesis studied the statistical inference problem for a second order linear stochastic ordinary differential equation using discrete observations. All cases for this equation are analyzed, and the limiting distributions for the estimation errors are attained and compared to the corresponding continuous-time results in every case. The thesis also discussed the simultaneous and sequential double asymptotics, which serve as a bridge between the discrete and continuous-time model