For more details, please refer to the link: https://sites.google.com/gs.ncku.edu.tw/wangwl/home/intellectual-contributions

ORCiD: https://orcid.org/0000-0002-0344-7954

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

2. 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, https://doi.org/10.1177/09622802221146312

3. 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

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

5. 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

6. 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

7. Lin, T.I., Chen, I.A. and Wang, W.L.* (2022) A robust factor analysis model based on the canonical fundamental skew-t distribution. Statistical Papers, https://doi.org/10.1007/s00362-022-01318-8

8. 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

9. 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.

10.  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

11.  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.

12.  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.

13.  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.

14.  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.

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

16.  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.

17.  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.

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., 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.

20.  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.

21.  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.

22.  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.

23.  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.

24.  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.

25.  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.

26.  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.

27.  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.

28.  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.

29.  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.

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

31.  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.

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

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.

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.

2. 王婉倫 (2019, 5月) 多元長期追蹤資料分群方法與應用。研究成果報導，自然科學簡訊，自然科學及永續研究發展司-科技部，第三十一卷第二期, 67-71。

3.   Ho, H.J., Lin, T.I., Wang, W.L., Garay, A.M., Lachos, V.H., and Castro, L.M. (2015) R TTmoment package: sampling and calculating the first and second moments for the doubly truncated multivariate t distribution. R package version 3.2.3, 2015-05-04.

1. 2018/08/01~2021/07/31, Funding of “Excellent Young Scholar Research Grants” from the Department of Natural Sciences and Sustainable Development, Ministry of Science and Technology”.

2.   2017, Outstanding Teaching Award from Feng Chia University, 2017/11/15.

3.  2010, Outstanding Prize of the Ching-Zong Wei Statistics Paper Award, granted by Institute of Statistics, National University of Kaohsiung and South Taiwan Statistics Conference.

4.  2007, Chinese Statistical Association Paper Award, granted by the Chinese Statistical Association.