Tsung-I Lin

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 20

Top Topics

Factor Analyzers

Incomplete Data

Maximum Likelihood

Top Venues

Comput. Stat. Data Anal.

J. Multivar. Anal.

Adv. Data Anal. Classif.

Publications

Wan-Lun Wang, Yu-Chen Yang, Tsung-I Lin
Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights. Adv. Data Anal. Classif. 18 (2) (2024)
Tsung-I Lin, Wan-Lun Wang
On moments of truncated multivariate normal/independent distributions. J. Multivar. Anal. 199 (2024)
Mehrdad Naderi, Mostafa Tamandi, Elham Mirfarah, Wan-Lun Wang, Tsung-I Lin
Three-way data clustering based on the mean-mixture of matrix-variate normal distributions. Comput. Stat. Data Anal. 199 (2024)
Mehrdad Naderi, Elham Mirfarah, Wan-Lun Wang, Tsung-I Lin
Robust mixture regression modeling based on the normal mean-variance mixture distributions. Comput. Stat. Data Anal. 180 (2023)
Wan-Lun Wang, Tsung-I Lin
Robust clustering via mixtures of t factor analyzers with incomplete data. Adv. Data Anal. Classif. 16 (3) (2022)
Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan
Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Adv. Data Anal. Classif. 15 (2) (2021)
Abbas Mahdavi, Vahid Amirzadeh, Ahad Jamalizadeh, Tsung-I Lin
Maximum likelihood estimation for scale-shape mixtures of flexible generalized skew normal distributions via selection representation. Comput. Stat. 36 (3) (2021)
Abbas Mahdavi, Vahid Amirzadeh, Ahad Jamalizadeh, Tsung-I Lin
A Multivariate Flexible Skew-Symmetric-Normal Distribution: Scale-Shape Mixtures and Parameter Estimation via Selection Representation. Symmetry 13 (8) (2021)
Yu-Chen Yang, Tsung-I Lin, Luis Mauricio Castro, Wan-Lun Wang
Extending finite mixtures of t linear mixed-effects models with concomitant covariates. Comput. Stat. Data Anal. 148 (2020)
Mostafa Tamandi, Ahad Jamalizadeh, Tsung-I Lin
-normal distributions: characterizations and estimation. Comput. Stat. 34 (1) (2019)
Jochen Einbeck, John Hinde, Salvatore Ingrassia, Tsung-I Lin, Paul D. McNicholas
Editorial for the 4th Special Issue on advances in mixture models. Comput. Stat. Data Anal. 132 (2019)
Wan-Lun Wang, Luis Mauricio Castro, Victor H. Lachos, Tsung-I Lin
Model-based clustering of censored data via mixtures of factor analyzers. Comput. Stat. Data Anal. 140 (2019)
Wan-Lun Wang, Luis Mauricio Castro, Yen-Ting Chang, Tsung-I Lin
Mixtures of restricted skew-t factor analyzers with common factor loadings. Adv. Data Anal. Classif. 13 (2) (2019)
Mehrdad Naderi, Wen-Liang Hung, Tsung-I Lin, Ahad Jamalizadeh
A novel mixture model using the multivariate normal mean-variance mixture of Birnbaum-Saunders distributions and its application to extrasolar planets. J. Multivar. Anal. 171 (2019)
Wan-Lun Wang, Luis Mauricio Castro, Tsung-I Lin
Automated learning of t factor analysis models with complete and incomplete data. J. Multivar. Anal. 161 (2017)
Ahad Jamalizadeh, Tsung-I Lin
A general class of scale-shape mixtures of skew-normal distributions: properties and estimation. Comput. Stat. 32 (2) (2017)
Wan-Lun Wang, Min Liu, Tsung-I Lin
Robust skew-t factor analysis models for handling missing data. Stat. Methods Appl. 26 (4) (2017)
Wan-Lun Wang, Tsung-I Lin
Maximum likelihood inference for the multivariate t mixture model. J. Multivar. Anal. 149 (2016)
Tsung-I Lin, Geoffrey J. McLachlan, Sharon X. Lee
Extending mixtures of factor models using the restricted multivariate skew-normal distribution. J. Multivar. Anal. 143 (2016)
John Hinde, Salvatore Ingrassia, Tsung-I Lin, Paul D. McNicholas
The Third Special Issue on Advances in Mixture Models. Comput. Stat. Data Anal. 93 (2016)
Wan-Lun Wang, Tsung-I Lin
Robust model-based clustering via mixtures of skew-t distributions with missing information. Adv. Data Anal. Classif. 9 (4) (2015)
Tsung-I Lin, Hsiu J. Ho, Chia-Rong Lee
Flexible mixture modelling using the multivariate skew-t-normal distribution. Stat. Comput. 24 (4) (2014)
Tsung-I Lin
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition. Comput. Stat. Data Anal. 71 (2014)
Wan-Lun Wang, Tsung-I Lin
-factor analyzers. Comput. Stat. 28 (2) (2013)
Hsiu J. Ho, Tsung-I Lin, Hannah H. Chang, Steven B. Haase, Sui Huang, Saumyadipta Pyne
Parametric modeling of cellular state transitions as measured with flow cytometry. BMC Bioinform. 13 (S-5) (2012)
Hsiu J. Ho, Saumyadipta Pyne, Tsung-I Lin
Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms. Stat. Comput. 22 (1) (2012)
Saumyadipta Pyne, Steven B. Haase, Hsiu J. Ho, Tsung-I Lin
Parametric modeling of cellular state transitions as measured with flow cytometry. ICCABS (2011)
Elizabeth Rossin, Tsung-I Lin, Hsiu J. Ho, Steven J. Mentzer, Saumyadipta Pyne
A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues. Bioinform. 27 (19) (2011)
Tsung-I Lin
distributions. Stat. Comput. 20 (3) (2010)
Tzy-Chy Lin, Tsung-I Lin
Supervised learning of multivariate skew normal mixture models with missing information. Comput. Stat. 25 (2) (2010)
Saumyadipta Pyne, Xinli Hu, Kui Wang, Elizabeth Rossin, Tsung-I Lin, Lisa Maier, Clare Baecher-Allan, Geoffrey J. McLachlan, Pablo Tamayo, David Hafler, Philip L. De Jager, Jill P. Mesirov
Automated High-Dimensional Flow Cytometric Data Analysis. RECOMB (2010)
Tsung-I Lin
Maximum likelihood estimation for multivariate skew normal mixture models. J. Multivar. Anal. 100 (2) (2009)
Tsung-I Lin, Hsiu J. Ho, Chiang L. Chen
Analysis of multivariate skew normal models with incomplete data. J. Multivar. Anal. 100 (10) (2009)
Tsung-I Lin, Hsiu J. Ho, Pao-Sheng Shen
mixture models with missing information. Comput. Stat. 24 (3) (2009)
Tsung-I Lin, Jack C. Lee, Wan J. Hsieh
distribution. Stat. Comput. 17 (2) (2007)
Tsung-I Lin, Jack C. Lee, Hsiu J. Ho
On fast supervised learning for normal mixture models with missing information. Pattern Recognit. 39 (6) (2006)
Tsung-I Lin, Jack C. Lee, Huey F. Ni
Bayesian analysis of mixture modelling using the multivariate t distribution. Stat. Comput. 14 (2) (2004)