INTELLECTUAL CONTRIBUTIONS

Publications

1. Lin, T.I. and Wang, W.L.* (2024) On moments of truncated multivariate normal/independent distributions. Journal of Multivariate Analysis, 199, 105248. https://doi.org/10.1016/j.jmva.2023.105248

2. Wang, W.L., Castro, L.M., Li, H.J. and Lin, T.I.* (2023) Mixtures of t factor analyzers with censored responses and external covariates: An application to educational data from Peru. British Journal of Mathematical and Statistical Psychology, https://doi.org/10.1111/bmsp.12329.

3. Wang, W.L., Castro, L.M. and Lin, T.I.* (2023) Bayesian multivariate nonlinear mixed models for censored longitudinal trajectories with non-monotone missing values. Metrika, https://doi.org/10.1007/s00184-023-00929-x

4. Wang, W.L. and Lin, T.I.* (2023) Model-based clustering via mixtures of unrestricted skew normal factor analyzers with complete and incomplete data. Statistical Methods & Applications, 32, 787-817. https://doi.org/10.1007/s10260-022-00674-x

5. Wang, W.L.* (2023) Multivariate contaminated normal censored regression model: properties and maximum likelihood inference. Journal of Computational and Graphical Statistics, 32(4), 1671-1684. https://doi.org/10.1080/10618600.2023.2184375

6. Lin, T.I., Chen, I.A. and Wang, W.L.* (2023) A robust factor analysis model based on the canonical fundamental skew-t distribution. Statistical Papers, 64, 367-393. https://doi.org/10.1007/s00362-022-01318-8

7. Naderi, M., Mirfarah, E., Wang, W.L. and Lin, T.I.* (2023) Robust mixture regression modeling based on the normal mean-variance mixture distributions. Computational Statistics and Data Analysis, 180, 107661. https://doi.org/10.1016/j.csda.2022.107661

8. Lin, T.I. and Wang, W.L.* (2023) Flexible modeling of multiple nonlinear longitudinal trajectories with censored and non-ignorable missing outcomes. Statistical Methods in Medical Research, 32(3), 593–608. https://doi.org/10.1177/09622802221146312

9. Mirfarah, E., Naderi, M., Lin, T.I. and Wang, W.L.* (2022) Multivariate measurement error models with normal mean-variance mixture distributions. Stat, 11(1), e503. https://doi.org/10.1002/sta4.503

10. Lin, T.I. and Wang, W.L.* (2022) Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies. Biometrical Journal, 64(7), 1325-1339 https://doi.org/10.1002/bimj.202100233

11. Wang, W.L. and Lin, T.I.* (2022) Robust clustering via mixtures of t factor analyzers with incomplete data. Advances in Data Analysis and Classification, 16, 659-690. https://doi.org/10.1007/s11634-021-00453-8

12. Wang, W.L., Yang, Y.C. and Lin, T.I.* (2022) Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights. Advances in Data Analysis and Classification, https://doi.org/10.1007/s11634-022-00502-w

13. Wang, W.L. and Lin, T.I.* (2022) Robust clustering of multiply censored data via mixtures of t factor analyzers. TEST, 31, 22-53. https://doi.org/10.1007/s11749-021-00766-y.

14. Wang, W.L., Castro, L.M., Hsieh, W.C. and Lin T.I.* (2021) Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables. Statistical Papers, 62(5), 2119–2145.

15. Galarza, C.E., Lin, T.I., Wang, W.L. and Lachos, V.H.* (2021) On moments of folded and truncated multivariate Student-t distributions based on recurrence relations. Metrika, 84, 825-850 https://doi.org/10.1007/s00184-020-00802-1.

16. Taavoni, M., Arashi, M.*, Wang, W.L. and Lin, T.I. (2021) Multivariate t semiparametric mixed-effects model for longitudinal data with multiple characteristics. Journal of Statistical Computation and Simulation, 91(2), 260-281. https://doi.org/10.1080/00949655.2020.1812608.

17. Wang, W.L., Jamalizadeh, A. and Lin T.I.* (2020) Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions. Statistical Papers, 61, 2643–2670.

18. Wang, W.L. and Lin, T.I.* (2020) Automated learning of mixtures of factor analysis models with missing information. TEST, 29:1098–1124. https://doi.org/10.1007/s11749-020-00702-6.

19. Wang, W.L.* (2020) Bayesian analysis of multivariate linear mixed models with censored and intermittent missing responses. Statistics in Medicine, 39(19), 2518–2535.

20. Yang, Y.C., Lin, T.I., Castro, L.M. and Wang, W.L.* (2020) Extending finite mixtures of linear mixed-effects models with concomitant covariates. Computational Statistics and Data Analysis, 148, 106961. https://doi.org/10.1016/j.csda.2020.106961

21. Lin, T.I. and Wang, W.L.* (2020) Multivariate-t linear mixed models with censored responses, intermittent missing values and heavy tails. Statistical Methods in Medical Research, 29(5), 1288–1304. [SCI] 

22. Wang, W.L., Castro, L.M., Lachos, V.H. and Lin, T.I.* (2019) Model-based clustering of censored data via mixtures of factor analyzers. Computational Statistics and Data Analysis, 140, 104–121.

23. Wang, W.L., Castro, L.M., Chang, Y.T. and Lin, T.I.* (2019) Mixtures of restricted skew-t factor analyzers with common factor loadings. Advances in Data Analysis and Classification, 13(2), 445–480.

24. Castro, L.M.*, Wang, W.L., Lachos, V.H., Carvalho, V.I. and Bayes, C.L. (2019) Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness. Statistical Methods in Medical Research, 28(5), 1457–1476.

25. Wang, W.L.* (2019) Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values. TEST, 28(1), 196–222.

26. Lin, T.I., Lachos, V.H., Wang, W.L.* (2018) Multivariate longitudinal data analysis with censored and intermittent missing responses. Statistics in Medicine, 37(19), 2822–2835.

27. Lin, T.I.*, Wang, W.L., McLachlan, G.J. and Lee, S.X. (2018) Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18(1), 50–72.

28. Wang, W.L.* and Castro, L.M. (2018) Bayesian inference on multivariate-t nonlinear mixed-effects models for multiple longitudinal data with missing values. Statistics and Its Interface, 11(2), 251–264.

29. Wang, W.L.*, Lin, T.I. and Lachos, V.H. (2018) Extending multivariate-t linear mixed models for multiple longitudinal data with censored responses and heavy tails. Statistical Methods in Medical Research, 27(1), 48–64.

30. Wang, W.L., Liu, M. and Lin, T.I.* (2017) Robust skew-t factor analysis models for handling missing data. Statistical Methods and Applications, 26(4), 649–672.

31. Lin, T.I. and Wang, W.L.* (2017) Multivariate-t nonlinear mixed models with application to censored multi-outcome AIDS studies. Biostatistics, 18(4), 666–681.

32. Wang, W.L., Castro, L.M., and Lin, T.I.* (2017) Automated learning of t factor analysis models with complete and incomplete data. Journal of Multivariate Analysis, 161, 157–171.

33. Wang, W.L.* (2017) Mixture of multivariate-t linear mixed models for multi-outcome longitudinal data with heterogeneity. Statistica Sinica, 27, 733–760.

34. Wang, W.L. and Lin, T.I.* (2017) Flexible clustering via extended mixtures of common t-factor analyzers. AStA Advances in Statistical Analysis, 101, 227–252.

35. Wang, W.L.* and Lin, T.I. (2016) Maximum likelihood inference for the multivariate t mixture model. Journal of Multivariate Analysis, 149, 54-64.

36. Wang, W.L. and Lin, T.I.* (2015) Robust model-based clustering via mixtures of skew-t distributions with missing information. Advances in Data Analysis and Classification, 9, 423-445. 

37. Wang, W.L.* (2015) Approximate methods for maximum likelihood estimation of multivariate nonlinear mixed-effects models. Entropy, 17, 5353-5381.

38. Wang, W.L.* and Lin, T.I. (2015) Bayesian analysis of multivariate t linear mixed models with missing responses at random. Journal of Statistical Computation and Simulation, 85(17), 3594-3612.

39. Wang, W.L.* (2015) Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values. Computational Statistics and Data Analysis, 83, 223-235.

40. Wang, W.L.* and Lin, T.I. (2014) Multivariate t nonlinear mixed-effects models for multi-outcome longitudinal data with missing values. Statistics in Medicine, 33(17), 3029-3046.

41. Wang, W.L.* (2013) Mixtures of common factor analyzers for high-dimensional data with missing information. Journal of Multivariate Analysis, 117, 120-133.

42. Wang, W.L. and Lin, T.I.* (2013) An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers. Computational Statistics, 28 (2), 751-769.

43. Wang, W.L.* (2013) Multivariate t linear mixed models for irregularly observed multiple repeated measures with missing outcomes. Biometrical Journal, 55 (4), 554-571.

44. Lin, T.I. and Wang, W.L.* (2013) Multivariate skew-normal linear mixed models for multi-outcome longitudinal data. Statistical Modelling, 13 (3), 199-221.

45. Wang, W.L.* and Fan, T.H. (2012) Bayesian analysis of multivariate t linear mixed models using a combination of IBF and Gibbs samplers. Journal of Multivariate Analysis, 105 (1), 300-310.

46. Ho, H.J., Lin, T.I., Chen, H.Y., and Wang, W.L.* (2012) Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142 (1), 25-40.

47. Wang, W.L.* and Fan, T.H. (2011) Estimation in multivariate t linear mixed models for multiple longitudinal data. Statistica Sinica, 21 (4), 1857-1880.

48. Fan, T.H. and Wang, W.L.* (2011) Accelerated life tests for Weibull series systems with masked data. IEEE Transactions on RELIABILITY, 60 (3), 557-569.

49. Lin, T.I.* and Wang, W.L. (2011) Bayesian inference in joint modeling of location and scale parameters of the t distribution for longitudinal data. Journal of Statistical Planning and Inference, 141 (4), 1543-1553.

50. Wang, W.L.* and Fan, T.H. (2010) ECM-based maximum likelihood inference for multivariate linear mixed models with autoregressive errors. Computational Statistics and Data Analysis, 54 (5), 1328-1341.

51. Fan, T.H.*, Wang, W.L. and Balakrishnan, N. (2008) Exponential progressive step-stress life-testing with link function based on Box-Cox transformation. Journal of Statistical Planning and Inference, 138 (8), 2340-2354.

Book Chapter

1. Naderi, M., Jamalizadeh, A., Wang, W.L. and Lin T.I.* (2020) Evaluating risk measures using the normal mean-variance Birnbaum-Saunders distribution. In: Bekker A., Chen G., Ferreira J. (eds) Computational and Methodological Statistics and Biostatistics. Emerging Topics in Statistics and Biostatistics. Springer, Cham, 187-209. https://doi.org/10.1007/978-3-030-42196-0_8. 

Proceedings

1. Wang, W.L.* (2015) Approximate methods for maximum likelihood estimation of multivariate nonlinear mixed-effects models. Proceedings of the 60th World Statistics Congress – ISI2015, July 26-31, 2015 in Rio de Janeiro, RJ, Brazil.

2. Lin, T.I.* and Wang, W.L. (2015) Bayesian computational strategies for multivariate t linear mixed models with missing outcomes. Proceedings of the 60th World Statistics Congress – ISI2015, July 26-31, 2015 in Rio de Janeiro, RJ, Brazil.

3. Wang, W.L.* and Fan, T.H. (2011). Bayesian inference in multivariate t linear mixed models using the IBF-Gibbs sampler. Section on Quality and Productivity – JSM 2011 Proceedings, Aug., Miami, Florida, USA. 523-535.

4. Wang, W.L.* and Fan, T.H. (2010). Multivariate t linear mixed models with AR(p) errors for multiple longitudinal data. Section on Statistical Computing – JSM 2010 Proceedings, Aug., Vancouver, BC, Canada. 649-663.

5. Wang, W.L.* and Fan, T.H. (2009). Test and prediction in multivariate linear mixed models for multiple longitudinal data. Section on Statistical Computing – JSM 2009 Proceedings, Aug., Washington, D.C., USA. 546-559.

6. Fan, T.H.* and Wang, W.L. (2007). Bayesian inference for progressive step-stress life-testing with the Box-Cox transformation. ISI 2007, Aug., Lisbon, Portugal.

Conferences

1. Wang, W.L.* and Lin, T.I. (2023) Extending multivariate nonlinear mixed models with censored and non-ignorable missing outcomes. 16th International Conference of the ERCIM WG on Computational and Methodological Statistics; 17th International Conference on Computational and Financial Econometrics (CFE-CMStatistics 2023), Session on EO118: Modern Methods and Computational Techniques for Multifaced Data, December 16-18, 2023 in HTW Berlin, University of Applied Sciences, Berlin, Germany. (Invited Speaker)

2. Wang, W.L.* and Lin, T.I. (2023) A selection approach to multivariate linear mixed models with censored and non-ignorable missing responses. The 64th ISI World Statistics Congress, Session on IPS 92: Innovative Nonregular Approaches to Statistical Modelling for Complex Data, July 16-20, 2023 in Shaw Centre at Ottawa, Canada. (Invited Speaker)

3. Wang, W.L.* and Lin, T.I. (2023) A selection model for multivariate longitudinal data with censored and nonignorable intermittent missing outcomes. The 32nd South Taiwan Statistics Conference and 2023 Chinese Institute of Probability Statistics Annual Meeting, Session on Computational Statistics, June 29-30, 2023 in National Dong Hwa University, Hualien, Taiwan. (Invited Speaker) 

4. Wang, W.L.* and Lin, T.I. (2022) Multivariate linear mixed models with censored and nonignorable missing outcomes. 15th International Conference of the ERCIM WG on Computational and Methodological Statistics; 16th International Conference on Computational and Financial Econometrics (CFE-CMStatistics 2022), Session on EO614: Non-regular Techniques for Statistical Modeling and Computing  (Virtual), December 17-19, 2022 in King's College London, UK.  (Invited speaker)

5. Wang, W.L.* (2022) Analysis of multivariate longitudinal data with censored responses, missing values and heavy tails. International Joint Symposium on Applied Mathematics, Science and Technology, Session on Applied Mathematics, December 1, 2022 in National Chung Hsing University, Taichung, Taiwan. (Invited Speaker)

6. Wang, W.L.* (2022) Bayesian analysis of multivariate linear mixed models with censored and missing responses. The 24th International Conference on Computational Statistics (COMPSTAT 2022), Session on CO047: Statistical Methods for Statistically Challenging Data (Virtual), August 23-26, 2022 in University of Bologna, Italy. (Invited speaker)

7. Wang, W.L.* (2022) Multivariate-t linear mixed models for longitudinal data with censored and intermittent missing responses. The 31th South Taiwan Statistics Conference and 2022 Chinese Institute of Probability Statistics Annual Meeting, Session on Computational Statistics, July 28-29, 2022 in Feng Chia University, Taichung, Taiwan. (Invited Speaker)

8. Wang, W.L.* (2022) Multivariate-t linear mixed models for longitudinal data with censored and intermittent missing responses. 5th International Conference on Econometrics and Statistics (EcoSta 2022), Session on EO023: Modern Multivariate Methods for Multifaceted Data, June 4-6, 2022 in Hybrid Conference hosted by Ryukoku University, Kyoto, Japan. (Invited Speaker) 

9. Wang, W.L.* and Lin, T.I. (2021) Multivariate-t linear mixed models for longitudinal data with censored and intermittent missing responses. 14th International Conference of the ERCIM WG on Computational and Methodological Statistics and 15th International Conference on Computational and Financial Econometrics (CFE-CMStatistics 2021), Session on EO444: Recent Developments on Non-regular Statistical Modeling with Complete and Incomplete Data, December 18-20, 2021 in Hybrid Conference hosted by King's College London, UK. (Invited Speaker)

10. Wang, W.L.* (2021) Bayesian inference on multivariate linear mixed models with censored and missing responses at random. The 30th South Taiwan Statistics Conference and 2021 Chinese Institute of Probability Statistics Annual Meeting, Session on Bayesian Analysis, October 30-31, 2021 in National University of Kaohsiung, Kaohsiung, Taiwan. (Invited Speaker) 

11. Wang, W.L.* (2021) A Bayesian approach to multivariate linear mixed models with censored and missing responses. 2021 Cross-Strait Conference on Statistics and Probability, Session on Multivariate Analysis and Machine Learning, July 31, 2021 in Virtual Conference hosted by National Chengchi University, Taipei City, Taiwan. (Invited Speaker) 

12. Wang, W.L.* (2021) Clustering incomplete longitudinal data via finite mixtures of multivariate t nonlinear mixed model. Data Science, Statistics & Visualisation and the European Conference on Data Analysis (DSSV-ECDA 2021), Session on Computing and Modeling for Longitudinal Data Analysis, July 7-9, 2021 in Virtual Conference hosted by Erasmus University Rotterdam, Netherlands. (Invited Speaker)

13. Wang, W.L.* (2021) Mixtures of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missingness. 4th International Conference on Econometrics and Statistics (EcoSta 2021), Session on EO033: Novel Statistical Modeling and Computing Methods for Complex Data, June 24-26, 2021 in Virtual Conference hosted by Hong Kong University of Science and Technology, Hong Kong. (Invited Speaker)

14. Wang, W.L.* (2020) Clustering multiple longitudinal data via finite mixtures of multivariate t nonlinear mixed model. 109 Statistical Academic Conference, Session on Longitudinal Data Analysis, December 19, 2020 in Academia Sinica, Taipei, Taiwan. (Session Organizer, Chair and Invited Speaker)

15. Wang, W.L.*, Lin, T.I. and Lachos, V.H. (2019) Analysis of multivariate longitudinal data with censored and intermittent missing responses. The 11th ICSA International Conference, Session on (S100) New Advances on Statistical Modeling of Complex Data, December 20-21, 2019 in Zhejiang University, Hangzhou, China. (Invited Speaker)

16. Wang, W.L.* (2019) Bayesian approach to multivariate-t nonlinear mixed models with missing responses at random. International Symposium on Statistics and Biostatistics (ISBS), July 19-20, 2019 in University of Pretoria, South Africa. (Keynote Speaker; Session Chair)

17. Wang, W.L.* and Castro, L.M. (2019) Bayesian inference on multivariate-t nonlinear mixed-effects models for multiple longitudinal data. 3rd International Conference on Econometrics and Statistics (EcoSta 2019), Session on EO089: Recent Advances in Complex Data Modeling, June 25-27, 2019 in National Chung Hsing University, Taichung, Taiwan. (Local Organizing Committee; Session Organizer and Chair; Invited Speaker)

18. Wang, W.L.* and Castro, L.M. (2019) Bayesian analysis of multivariate-t nonlinear mixed-effects models with missing responses. The 28th South Taiwan Statistics Conference and 2019 Chinese Institute of Probability Statistics Annual Meeting, Session on Bayesian Analysis, June 21-22, 2019 in National Chung Hsing University, Taichung, Taiwan. (Invited Speaker)

19. Wang, W.L.* and Lin, T.I. (2018) Multivariate-t nonlinear mixed models for censored multi-outcome longitudinal data. 11th International Conference of the ERCIM WG on Computational and Methodological Statistics; 12th International Conference on Computational and Financial Econometrics (CFE-CMStatistics 2018), Session on New Advances on Statistical Modeling of Complex Data I, December 14-16, 2018 in University of Pisa, Italy. (Invited speaker)

20. Wang, W.L.* (2018) Finite mixture of multivariate t linear mixed models for multi-outcome longitudinal data. The 14th Iranian Statistics Conference (ISC14), Session on Invited Talk Session, August 25-27, 2018 in Shahrood University of Technology, Shahrud, Iran. (Keynote Speaker)

21. Wang, W.L.* (2018) Mixtures of multivariate t linear mixed models for clustering multiple longitudinal trajectories. Advances in Finite Mixture and other Non-regular Models, Session on A3：Mixture of Regression Models and Applications, August 12-16, 2018 in Guangxi Normal University, China. (Invited speaker)

22. Wang, W.L.* (2018) Clustering multi-outcome longitudinal data via finite mixtures of multivariate t linear mixed models. 2nd Internal Conference on Econometrics and Statistics (EcoSta 2018), Session on Mixture Models for Censored and Longitudinal Data. June 19-21, 2018 in City University of Hong Kong, Hong Kong. (Invited speaker)

23. Wang, W.L.*, Lin, T.I. and Lachos, V.H. (2017) Multivariate-t linear mixed models for multiple repeated measures with censored data. 11th International Conference on Computational and Financial Econometrics; 10th International Conference of the ERCIM WG on Computational and Methodological Statistics (CFE-CMStatistics 2017), Session on Complex Data Modeling and Computational Methods, December 16-18, 2017 in Senate House and Birkbeck University of London, UK. (Invited speaker) 

24. Wang, W.L.* (2017) Mixtures of common t-factor analyzers for high-dimensional data with missing information. Conference of the International Federation of Classification Societies (IFCS 2017), Session on SP04: Clustering with Mixture Models, August 8-10, 2017 in Tokai University, Tokyo, Japan. (Invited speaker)

25. Wang, W.L.*, Lin, T.I., and Lachos, V.H. (2017) Multivariate-t linear mixed models for multiple longitudinal data with censorship and fat-tailed behavior. 1st Internal Conference on Econometrics and Statistics (EcoSta 2017), Session on New Advances in Statistical Modeling, Computation and Applications, June 15-17, 2017 in Hong Kong University of Science and Technology, Hong Kong. (Invited speaker)

26. Wang, W.L.* (2017) Multivariate-t linear mixed models for multiple longitudinal data with censored responses. The 13th Cross-Strait Conference on Applied Statistics, April 23-24, 2017 in Tunghai University, Taichung, Taiwan. (Invited speaker)

27. Wang, W.L.* (2016) Analysis of incomplete high-dimensional data via mixtures of common t-factor analyzers. 10th International Conference on Computational and Financial Econometrics, 9th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CFE-CMStatistics 2016), Session on Statistical Modeling for High-dimensional and Biomedical Data, December 9-11, 2016 in University of Seville, Spain. (Invited speaker)

28. Wang, W.L.* (2016) Approximate maximum likelihood approaches for multivariate nonlinear mixed-effects models. 2016 The 10th Cross-Strait Conference on Statistics and Probability, August 11-13, 2016 in University of Electronic Science and Technology, Chengdu, Sichuan. (Invited speaker)

29. Wang, W.L.* (2015) Approximate methods for maximum likelihood estimation of multivariate nonlinear mixed-effects models. The 60th World Statistics Congress – ISI2015, Session on statistical modeling of multi-level multivariate data using Kronecker product structured covariance matrices, July 26-31, 2015 in Rio de Janeiro, RJ, Brazil.

30. Wang, W.L.* (2015) Mixtures of common factor analyzers for high-dimensional data with missing values. The 24th International Workshop on Matrices and Statistics – IWMS-2015, Session on statistical modeling and computation, May 25-28, 2015 in Haikou, China. (Invited speaker)

31. Wang, W.L.* (2014) Some new tools for mixtures of common t-factor analyzers with its application. The 2nd International Workshop on Model-Based Clustering and Classification – MBC2, Session on new developments in non-Gaussian mixture modeling, September 3-5, 2014 in Dipartimento di Economia e Impresa, Università di Catania, Catania, Italy. (Invited speaker)

32. Wang, W.L.* and Lin, T.I. (2014) Estimation in multivariate t nonlinear mixed-effects models with missing outcomes. The International Conference on Trends and Perspectives in Linear Statistical Inference – LinStat 2014, Session on the use of Kronecker product in statistical modeling, August 24-28, 2014 in Linköping University, Linköping, Sweden. (Invited speaker)

33. Wang, W.L.* (2014) Analysis of incomplete high-dimensional data via mixtures of common factor analyzers. The 3rd Institute of Mathematical Statistics Asia Pacific Rim Meeting – IMS-APRM 2014, June 30 - July 3, 2014 in Taipei, Taiwan. (Invited speaker)

34. Wang, W.L.* (2013) Maximum likelihood inference for mixtures of common factor analyzers with missing information. The 6th International Conference of the ERCIM Working Group on Computational and Methodological Statistics, 7th CSDA International Conference on Computational and Financial Econometrics – CFE-ERCIM 2013, December 14-16, 2013 in University of London, UK. (Invited speaker)

35. Wang, W.L.* (2014) Computational strategies for mixtures of common t-factor analyzers. The 9th Cross-Strait Conference on Probability and Statistics, May 16-18, 2014 in Feng Chia University and National Cheng Hsing University, Taichung, Taiwan. (Invited speaker)

36. Wang, W.L.* (2014) Mixtures of common factor analyzers for clustering of incomplete high-dimensional data. 2014 Annual Meeting of Chinese Statistical Society and International Statistical Conference, December 6, 2014 in National Chiao Tung University, Hsinchu, Taiwan. (Invited speaker)

37. Wang, W.L.* (2011) Accelerated life tests for Weibull series systems with masked data. International Conference on Advances in Probability and Statistics - Theory and Applications: A Celebration of N. Balakrishnan's 30 years of Contributions to Statistics, December 28-31, 2011 in The Chinese University of Hong Kong, Hong Kong. (Invited Speaker)

38. Wang, W.L.* (2011) Reliability analysis of series systems with masking and censoring in step-stress life tests. 2011 Cross-Strait Conference on Applied Statistics, May 15-17, 2011 in Feng Chia University, Taichung, Taiwan. (Invited Speaker)

39. Wang, W.L.* (2010) Bayesian analysis of multivariate t linear mixed models using the IBF-Gibbs sampler. 2010 Annual Meeting of Chinese Statistical Society and International Statistical Conference, December 16-17, 2010 in National Central University, Jhongli, Taiwan. (Invited Speaker)

40. Wang, W.L.* (2010) Estimation in multivariate t linear mixed models for multiple longitudinal data. The 19th South Taiwan Statistics Conference and 2010 Cross-Strait Conference on Probability and Statistics, July 6-7, 2010 in National Cheng Kung University, Tainan, Taiwan. (Student Competition)

Seminar

1. Multivariate-t linear mixed models for longitudinal data with censored and intermittent missing responses. Regular Seminar in Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan, 2021/04/15.

2. Multivariate-t nonlinear mixed models for censored multi-outcome longitudinal data. Regular Seminar in Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. 2019/08/05.

3. Analysis of incomplete high-dimensional data using mixtures of common factor analyzers. Department of Statistics, Tunghai University. 2015/04/14.

4. Bayesian inference in multivariate t linear mixed models using the IBF-Gibbs sampler. Institute of Statistics, National Chiao Tung University. 2011/03/25.

5. Multivariate t linear mixed models with autoregressive errors for multiple longitudinal data. Institute of Statistical Science, Academia Sinica. 2010/11/22.

Funded Research Projects

1. MOST Grant: (Capacity: Principal Investigator)

Title: Non-Gaussian Modeling of Complex Longitudinal Data and Its Applications.

Grant number: MOST 110-2118-M-006-006-MY3. Funding Period: 2021/08/01 ~ 2024/07/31.

                               MOST 111-2811-M-006-006-MY2 (Recruitment of postdoctoral research fellow, Dr. Elham Mirfarah)

2. “Excellent Young Scholar Research Grants” from the Department of Natural Sciences and Sustainable Development, Ministry of Science and Technology (MOST)  (Capacity: Principal Investigator)

Title: Analysis of Multivariate Longitudinal Data with Censored and Missing Responses.

Grant number: MOST 107-2628-M-035-001-MY3. Funding Period: 2018/08/01 ~ 2021/07/31.

3. MOST Grant:  (Capacity: Principal Investigator)

Title: Mixture of Multivariate T Mixed-effects Models for Multiple Longitudinal Study with Heterogeneity. 

Grant number: MOST 105-2118-M-035-004-MY2. Funding Period: 2016/08/01 ~ 2018/07/31.

4. MOST Grant:  (Capacity: Principal Investigator)

Title: Multivariate T Nonlinear Mixed-Effects Models for Longitudinal Data with Multiple Outcomes. 

Grant number: MOST 103-2118-M-035-001-MY2. Funding Period: 2014/08/01 ~ 2016/07/31

5. NSC Grant:  (Capacity: Principal Investigator)

Title: Multivariate Mixed Models for Multi-outcome Longitudinal Data with Missing Values.

Grant number: NSC 101-2118-M-035 -003 -MY2. Funding Period: 2012/08/01 ~ 2014/07/31

6. NSC Grant:  (Capacity: Principal Investigator)

Title: Mixtures of t Factor Analyzers with Common Factor Loadings for High Dimensional Data. 

Grant number: NSC100-2118-M-035-002. Funding Period: 2011/08/01 ~ 2012/07/31

7. NSC Grant:  (Capacity: Principal Investigator)

Title: Bayesian analysis of multivariate t linear mixed models using the IBF-Gibbs sampler. 

Grant number: NSC99-2118-M-035-004. Funding Period: 2010/09/01 ~ 2011/07/31