Estimation for binary models generated by Gaussian autoregressive processes
Regression-type and partial likelihood models are presented for binary data obtained by clipping a Gaussian autoregressive process. Five methods for estimating parameters of the model are proposed and compared via a simulation study. A real data analysis is also presented.
autoregressive process partial likelihood binary data maximum likelihood gaussian process