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2020 – today
- 2024
[j36]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): 271-307 (2024)- [j35]Abbas Mahdavi, Anthony F. Desmond, Ahad Jamalizadeh, Tsung-I Lin:
Skew Multiple Scaled Mixtures of Normal Distributions with Flexible Tail Behavior and Their Application to Clustering. J. Classif. 41(3): 620-649 (2024) - [j34]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: 108016 (2024) - [j33]Tsung-I Lin, Wan-Lun Wang:
On moments of truncated multivariate normal/independent distributions. J. Multivar. Anal. 199: 105248 (2024) - 2023
- [j32]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: 107661 (2023) - 2022
- [j31]Wan-Lun Wang, Tsung-I Lin:
Robust clustering via mixtures of t factor analyzers with incomplete data. Adv. Data Anal. Classif. 16(3): 659-690 (2022) - 2021
- [j30]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): 481-512 (2021) - [j29]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): 2201-2230 (2021) - [j28]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): 1343 (2021) - 2020
- [j27]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: 106961 (2020)
2010 – 2019
- 2019
- [j26]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): 445-480 (2019) - [j25]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: 143-144 (2019) - [j24]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: 104-121 (2019) - [j23]Mostafa Tamandi, Ahad Jamalizadeh, Tsung-I Lin:
Shape mixtures of skew-t-normal distributions: characterizations and estimation. Comput. Stat. 34(1): 323-347 (2019) - [j22]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: 126-138 (2019) - 2017
- [j21]Ahad Jamalizadeh, Tsung-I Lin:
A general class of scale-shape mixtures of skew-normal distributions: properties and estimation. Comput. Stat. 32(2): 451-474 (2017) - [j20]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: 157-171 (2017) - [j19]Wan-Lun Wang, Min Liu, Tsung-I Lin:
Robust skew-t factor analysis models for handling missing data. Stat. Methods Appl. 26(4): 649-672 (2017) - 2016
- [j18]John Hinde, Salvatore Ingrassia, Tsung-I Lin, Paul D. McNicholas:
The Third Special Issue on Advances in Mixture Models. Comput. Stat. Data Anal. 93: 2-4 (2016) - [j17]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: 398-413 (2016) - [j16]Wan-Lun Wang, Tsung-I Lin:
Maximum likelihood inference for the multivariate t mixture model. J. Multivar. Anal. 149: 54-64 (2016) - 2015
- [j15]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): 423-445 (2015) - 2014
- [j14]Tsung-I Lin:
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition. Comput. Stat. Data Anal. 71: 183-195 (2014) - [j13]Tsung-I Lin, Hsiu J. Ho, Chia-Rong Lee:
Flexible mixture modelling using the multivariate skew-t-normal distribution. Stat. Comput. 24(4): 531-546 (2014) - 2013
- [j12]Wan-Lun Wang, Tsung-I Lin:
An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers. Comput. Stat. 28(2): 751-769 (2013) - 2012
- [j11]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): S5 (2012) - [j10]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): 287-299 (2012) - 2011
- [j9]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): 2746-2753 (2011) - [c2]Saumyadipta Pyne, Steven B. Haase, Hsiu J. Ho, Tsung-I Lin:
Parametric modeling of cellular state transitions as measured with flow cytometry. ICCABS 2011: 147-152 - 2010
- [j8]Tzy-Chy Lin, Tsung-I Lin:
Supervised learning of multivariate skew normal mixture models with missing information. Comput. Stat. 25(2): 183-201 (2010) - [j7]Tsung-I Lin:
Robust mixture modeling using multivariate skew t distributions. Stat. Comput. 20(3): 343-356 (2010) - [c1]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: 577
2000 – 2009
- 2009
- [j6]Tsung-I Lin, Hsiu J. Ho, Pao-Sheng Shen:
Computationally efficient learning of multivariate t mixture models with missing information. Comput. Stat. 24(3): 375-392 (2009) - [j5]Tsung-I Lin:
Maximum likelihood estimation for multivariate skew normal mixture models. J. Multivar. Anal. 100(2): 257-265 (2009) - [j4]Tsung-I Lin, Hsiu J. Ho, Chiang L. Chen:
Analysis of multivariate skew normal models with incomplete data. J. Multivar. Anal. 100(10): 2337-2351 (2009) - 2007
- [j3]Tsung-I Lin, Jack C. Lee, Wan J. Hsieh:
Robust mixture modeling using the skew t distribution. Stat. Comput. 17(2): 81-92 (2007) - 2006
- [j2]Tsung-I Lin, Jack C. Lee, Hsiu J. Ho:
On fast supervised learning for normal mixture models with missing information. Pattern Recognit. 39(6): 1177-1187 (2006) - 2004
- [j1]Tsung-I Lin, Jack C. Lee, Huey F. Ni:
Bayesian analysis of mixture modelling using the multivariate t distribution. Stat. Comput. 14(2): 119-130 (2004)
Coauthor Index
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