On fast supervised learning for normal mixture models with missing information. - Tsung-I Lin; Jack C. Lee; Hsiu J. Ho (Pattern Recognit., 2006)

Computationally efficient learning of multivariate t mixture models with missing information. - Tsung-I Lin; Hsiu J. Ho; Pao-Sheng Shen (Comput. Stat., 2009)

Analysis of multivariate skew normal models with incomplete data. - Tsung-I Lin; Hsiu J. Ho; Chiang L. Chen (J. Multivar. Anal., 2009)

Parametric modeling of cellular state transitions as measured with flow cytometry. - Saumyadipta Pyne; Steven B. Haase; Hsiu J. Ho; Tsung-I Lin (ICCABS, 2011)

A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues. - Elizabeth Rossin; Tsung-I Lin; Hsiu J. Ho; Steven J. Mentzer; Saumyadipta Pyne (Bioinform., 2011)

Parametric modeling of cellular state transitions as measured with flow cytometry. - Hsiu J. Ho; Tsung-I Lin; Hannah H. Chang; Steven B. Haase; Sui Huang; Saumyadipta Pyne (BMC Bioinform., 2012)

Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms. - Hsiu J. Ho; Saumyadipta Pyne; Tsung-I Lin (Stat. Comput., 2012)

Flexible mixture modelling using the multivariate skew-t-normal distribution. - Tsung-I Lin; Hsiu J. Ho; Chia-Rong Lee (Stat. Comput., 2014)