In this work, an analytical framework is developed for bias and variance in the kinetic parameter estimations with spatial regularization in dynamic positron emission tomography. Time consuming Monte Carlo simulations can be used for this purpose. In this work, a faster analytical framework is developed for bias and variance analysis in kinetic parameter estimations with spatial regularization. In addition validation experiments are performed on simulation data. It is observed that the bias and variance values obtained from Monte Carlo simulations and analytical calculations are consistent. These results indicate that the bias and variance in the kinetic parameter estimations with spatial regularization can be computed using the analytical framework that is derived in this work.