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.

• 科技部優秀年輕學者研究計畫 (107/08/01 ~ 110/07/31)
• 106學年度逢甲大學教學傑出獎教師
• 99年度魏慶榮統計論文獎優等(第十九屆南區統計研討會)
• 96年度中國統計學社論文獎-佳作獎
• 96年、98年斐陶斐榮譽會員 (國立中央大學分會推薦)

@FCU

指導學生獲111年度中國統計學社論文獎-優等獎 (研究生：李亞靜)；佳作獎(研究生：龔于涵)

指導學生獲110年度中國統計學社論文獎-優等獎 (研究生：劉佩萱)

指導學生獲109年度中國統計學社論文獎-佳作獎 (研究生：李易聰)

指導學生獲第三屆全國碩士生統計研究成果海報競賽特優獎 (研究生：劉俊賢)

指導學生獲107年度中國統計學社論文獎-優等獎 (研究生：楊郁成)

指導學生獲106年度中國統計學社論文獎-佳作獎 (研究生：黃彥菱、黃筠庭)

指導106年度科技部大專生專題研究計畫-由巨量資料分析探討台灣高齡化下的社會參與 (學生：許家欣)

指導105年度科技部大專生專題研究計畫-由巨量資料探討各國觀光產業發展之研究 (學生：陳啟昌)

指導103年度科技部大專生專題研究計畫-混合迴歸模型及其應用研究 (學生：楊雅惠)

指導學生獲102年度中國統計學社論文獎-佳作獎 (研究生：徐偉舜)