Estimation of Regression Parameters of Gaussian Markov Processes

Let {Ω, F, Ρ} be a complete probability space and let for t Є[0,∞) $$y\left( t \right)=X\left( t \right)\beta +z\left( t \right),y\left( 0 \right)=0$$ (1) be an n-dimensional continuous stochastic process. X(t) stands for a matrix of known functions, β for a p-dimensional vector of unknown parameters and z(t) for an n-dimensional Gaussian Markov process with expectation zero and covariance matrix R(t,s).Let Ro(t)=R(t,t).