In this paper, loss is taken to be proportional to squared error with the constant of proportionality equal to the square of the inverse of a scale parameter, and an invariant estimator is defined to be one with risk invariant under transformations of location and scale. For certain classes of estimators, best (minimum-mean-squared-error) invariant estimators are found for specified linear functions of an unknown scale parameter and one or more unknown location parameters. Even when the specified function is equal to a single location parameter, the best invariant estimator is not equal to the best unbiased estimator in the class except for complete samples from certain distributions such as the Gaussian.