Wan-Lun Wang

Personal Details

First Name:	Wan-Lun
Middle Name:
Last Name:	Wang
Suffix:
RePEc Short-ID:	pwa452
	[This author has chosen not to make the email address public]

Affiliation

College of Business
Feng Chia University

Taichung, Taiwan
http://www.cob.fcu.edu.tw/
RePEc:edi:cbfcutw (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Wang, Wan-Lun, 2015. "Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 223-235.

   Cited by:

   1. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
   2. García-Escudero, Luis Angel & Gordaliza, Alfonso & Greselin, Francesca & Ingrassia, Salvatore & Mayo-Iscar, Agustín, 2016. "The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 131-147.
   3. Wang, Wan-Lun & Castro, Luis M. & Lin, Tsung-I, 2017. "Automated learning of t factor analysis models with complete and incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 157-171.
   4. Ma, Xuan & Zhao, Jianhua & Wang, Yue & Shang, Changchun & Jiang, Fen, 2023. "Robust factored principal component analysis for matrix-valued outlier accommodation and detection," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
   5. Wan-Lun Wang & Luis M. Castro & Yen-Ting Chang & Tsung-I Lin, 2019. "Mixtures of restricted skew-t factor analyzers with common factor loadings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 445-480, June.
   6. Lin, Tsung-I & McLachlan, Geoffrey J. & Lee, Sharon X., 2016. "Extending mixtures of factor models using the restricted multivariate skew-normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 398-413.
   7. Wraith, Darren & Forbes, Florence, 2015. "Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 61-73.

2. Wan-Lun Wang & Tsung-I Lin, 2015. "Robust model-based clustering via mixtures of skew-t distributions with missing information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 423-445, December.

   Cited by:

   1. Lachos, Víctor H. & Moreno, Edgar J. López & Chen, Kun & Cabral, Celso Rômulo Barbosa, 2017. "Finite mixture modeling of censored data using the multivariate Student-t distribution," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 151-167.
   2. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
   3. Wan-Lun Wang & Tsung-I Lin, 2020. "Automated learning of mixtures of factor analysis models with missing information," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1098-1124, December.
   4. Francisco H. C. Alencar & Larissa A Matos & Víctor H. Lachos, 2022. "Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 463-486, November.
   5. Hung Tong & Cristina Tortora, 2022. "Model-based clustering and outlier detection with missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 5-30, March.
   6. Wei, Yuhong & Tang, Yang & McNicholas, Paul D., 2019. "Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 18-41.
   7. Reyhaneh Rikhtehgaran & Iraj Kazemi, 2016. "The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 541-562, December.

3. Wang, Wan-Lun, 2013. "Mixtures of common factor analyzers for high-dimensional data with missing information," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 120-133.

   Cited by:

   1. Wan-Lun Wang & Luis M. Castro & Yen-Ting Chang & Tsung-I Lin, 2019. "Mixtures of restricted skew-t factor analyzers with common factor loadings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 445-480, June.
   2. Lin, Tsung-I & McLachlan, Geoffrey J. & Lee, Sharon X., 2016. "Extending mixtures of factor models using the restricted multivariate skew-normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 398-413.
   3. Wang, Wan-Lun, 2015. "Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 223-235.
   4. Zhao, Jianhua & Shi, Lei, 2014. "Automated learning of factor analysis with complete and incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 205-218.
   5. O’Hagan, Adrian & Murphy, Thomas Brendan & Gormley, Isobel Claire & McNicholas, Paul D. & Karlis, Dimitris, 2016. "Clustering with the multivariate normal inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 18-30.
   6. Wan-Lun Wang & Tsung-I Lin, 2017. "Flexible clustering via extended mixtures of common t-factor analyzers," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 227-252, July.

4. Wan-Lun Wang & Tsung-I Lin, 2013. "An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers," Computational Statistics, Springer, vol. 28(2), pages 751-769, April.

   Cited by:

   1. Wang, Wan-Lun & Castro, Luis M. & Lin, Tsung-I, 2017. "Automated learning of t factor analysis models with complete and incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 157-171.
   2. Tsung-I Lin & I-An Chen & Wan-Lun Wang, 2023. "A robust factor analysis model based on the canonical fundamental skew-t distribution," Statistical Papers, Springer, vol. 64(2), pages 367-393, April.
   3. Kim, Hea-Jung, 2018. "Bayesian hierarchical robust factor analysis models for partially observed sample-selection data," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 65-82.
   4. Wan-Lun Wang & Tsung-I Lin, 2020. "Automated learning of mixtures of factor analysis models with missing information," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1098-1124, December.
   5. Uchenna Chinedu Nduka, 2022. "Efficient and robust estimation for autoregressive regression models using shape mixtures of skewt normal distribution," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1519-1551, September.
   6. Anastasios Bellas & Charles Bouveyron & Marie Cottrell & Jérôme Lacaille, 2013. "Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 281-300, September.
   7. Mostafa Tamandi & Ahad Jamalizadeh & Tsung-I Lin, 2019. "Shape mixtures of skew-t-normal distributions: characterizations and estimation," Computational Statistics, Springer, vol. 34(1), pages 323-347, March.
   8. Wan-Lun Wang & Tsung-I Lin, 2022. "Robust clustering via mixtures of t factor analyzers with incomplete data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 659-690, September.
   9. Lin, Tsung-I & McLachlan, Geoffrey J. & Lee, Sharon X., 2016. "Extending mixtures of factor models using the restricted multivariate skew-normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 398-413.
   10. Wang, Wan-Lun, 2015. "Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 223-235.
   11. Lin, Tsung-I & McNicholas, Paul D. & Ho, Hsiu J., 2014. "Capturing patterns via parsimonious t mixture models," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 80-87.
   12. Zhao, Jianhua & Shi, Lei, 2014. "Automated learning of factor analysis with complete and incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 205-218.
   13. Tsung-I Lin & Pal Wu & Geoffrey McLachlan & Sharon Lee, 2015. "A robust factor analysis model using the restricted skew- $$t$$ t distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 510-531, September.
   14. Yang, Yu-Chen & Lin, Tsung-I & Castro, Luis M. & Wang, Wan-Lun, 2020. "Extending finite mixtures of t linear mixed-effects models with concomitant covariates," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
   15. Wan-Lun Wang & Tsung-I Lin, 2017. "Flexible clustering via extended mixtures of common t-factor analyzers," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 227-252, July.

5. Wang, Wan-Lun & Fan, Tsai-Hung, 2012. "Bayesian analysis of multivariate t linear mixed models using a combination of IBF and Gibbs samplers," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 300-310.

   Cited by:

   1. Firoozeh Rivaz & Majid Jafari Khaledi, 2015. "Bayesian spatial prediction of skew and censored data via a hybrid algorithm," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(9), pages 1993-2009, September.
   2. D. Concordet & R. Servien, 2014. "Individual prediction regions for multivariate longitudinal data with small samples," Biometrics, The International Biometric Society, vol. 70(3), pages 629-638, September.
   3. Özgür Asar & David Bolin & Peter J. Diggle & Jonas Wallin, 2020. "Linear mixed effects models for non‐Gaussian continuous repeated measurement data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1015-1065, November.

6. Wang, Wan-Lun & Fan, Tsai-Hung, 2010. "ECM-based maximum likelihood inference for multivariate linear mixed models with autoregressive errors," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1328-1341, May.

   Cited by:

   1. M. Taavoni & M. Arashi, 2021. "Kernel estimation in semiparametric mixed effect longitudinal modeling," Statistical Papers, Springer, vol. 62(3), pages 1095-1116, June.
   2. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
   3. Wang, Wan-Lun & Fan, Tsai-Hung, 2012. "Bayesian analysis of multivariate t linear mixed models using a combination of IBF and Gibbs samplers," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 300-310.
   4. Rhee, Anbin & Kwak, Min-Sun & Lee, Keunbaik, 2022. "Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
   5. Hsiao, Cheng & Zhou, Qiankun, 2015. "Statistical inference for panel dynamic simultaneous equations models," Journal of Econometrics, Elsevier, vol. 189(2), pages 383-396.
   6. D. Concordet & R. Servien, 2014. "Individual prediction regions for multivariate longitudinal data with small samples," Biometrics, The International Biometric Society, vol. 70(3), pages 629-638, September.
   7. Hong-Yan Jiang & Rong-Xian Yue, 2019. "Pseudo-Bayesian D-optimal designs for longitudinal Poisson mixed models with correlated errors," Computational Statistics, Springer, vol. 34(1), pages 71-87, March.
   8. Paolo Vidoni, 2017. "Improved multivariate prediction regions for Markov process models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 1-18, March.

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