Data-driven efficient score tests for deconvolution hypotheses

M A Langovoy
2008 Inverse Problems  
We consider testing statistical hypotheses about densities of signals in deconvolution models. A new approach to this problem is proposed. We constructed score tests for the deconvolution with the known noise density and efficient score tests for the case of unknown density. The tests are incorporated with model selection rules to choose reasonable model dimensions automatically by the data. Consistency of the tests is proved.
doi:10.1088/0266-5611/24/2/025028 fatcat:6a4wom6navgxlhzwxmi3i5piqu