Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic-epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely…[Read more]

The proposed correction does not require calibration of the set-up and any prior knowledge of the sample geometry or composition. This data driven correction yields improved CT reconstruction with limited data. The method is demonstrated with a mathematical simulation and applied to experimental data for validation.Under low nitrogen (N) supply,…[Read more]

The proposed correction does not require calibration of the set-up and any prior knowledge of the sample geometry or composition. This data driven correction yields improved CT reconstruction with limited data. The method is demonstrated with a mathematical simulation and applied to experimental data for validation.Under low nitrogen (N) supply,…[Read more]