Gaussian profile estimation in one dimension

We extend recent results for estimating the parameters of a one-dimensional Gaussian profile to two-dimensional profiles, deriving the exact covariance matrix of the estimated parameters. While the exact form is easy to compute, we provide a set of close approximations that allow the covariance to take on a simple analytic form. This not only provides new insight into the behavior of the estimation parameters, but also lays a foundation for clarifying previously published work. We also show how to calculate the parameter variances for the case of truncated sampling, where the profile lies near the edge of the array detector. Finally, we calculate expressions for the bias in the classical formulation of the problem and provide an approach for its removal. This allows us to show how the bias affects the problem of choosing an optimal pixel size for minimizing parameter variances.