On the identification of random variables from quantized observations

Abstract

We prove that the scale and shift parameters of a family of probability laws can be identified from quantized values, under appropriate assumptions. As an application, we show the consistency of the maximum likelihood estimator for the parameters of a quantized Gaussian autoregressive process.

Results substantially more general, proof of Lemma 2.4 radically simplified thanks to an anonymous referee