On the identification of random variables from quantized observations
classification
🧮 math.PR
keywords
quantizedparametersapplicationappropriateassumptionsautoregressiveconsistencyestimator
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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.
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