In　this　paper,　the　expectation　maximization　(EM)　algorithm　is　applied　to　the　modeling　of　the　nano-gold　immunochromatographic　assay　(nano-GICA)　via　available　time　series　of　the　measured　signal　intensities　of　the　test　and　control　lines.　The　model　for　the　nano-GICA　is　developed　as　the　stochastic　dynamic　model　that　consists　of　a　first-order　autoregressive　stochastic　dynamic　process　and　a　noisy　measurement.　By　using　the　EM　algorithm,　the　model　parameters,　the　actual　signal　intensities　of　the　test　and　control　lines,　as　well　as　the　noise　intensity　can　be　identified　simultaneously.　Three　different　time　series　data　sets　concerning　the　target　concentrations　are　employed　to　demonstrate　the　effectiveness　of　the　introduced　algorithm.　Several　indices　are　also　proposed　to　evaluate　the　inferred　models.　It　is　shown　that　the　model　fits　the　data　very　well.