Selected Publications
1. Lee S Y*, Xia Y M (2006). Maximum likelihood methods in treating outliers and symmetrically heavy-tailed distributions for nonlinear structural equation models with missing data, Psychometrika, 71:565-585.
2. Lee S Y,* Xia Y M (2008). A robust bayesian approach for structural equation models with missing data. Psychometrika, 3: 343-364.
3. Xia Y M, Song X Y*, Lee SY (2009). Robust model fitting for the non linear structural equation model under normal theory, British Journal of Mathematical and Statistical Psychology, 3: 529-568.
4. Song X Y*, Xia Y M, Lee SY (2009). Bayesian semiparametric analysis of structural equation models with mixed continuous and unordered categorical variables, Statistics in Medicine,17: 2253-2276.
5. Song X Y, Xia Y M, Pan J H and Lee, S Y (2011). Model comparison of Bayesian semiparametric and parametric structural equation models. Structural Equation Modeling - A Multidisciplinary Journal, 18: 55-72.
6. Xia Y M* and Liu, Y A and Fang Z (2010). Bayes analysis for generalized logistic regression model and its application for Forestry survival rate. Journal of Nanjing Forestry University, 34(2): 47-50.
7. Xia Y M* and Liu Y A (2010). Bayesian semiparametric analysis for generalized linear latent variable model. The proceedings of 2010 international conference on probability and statistics of the international institute for general systems studies. Advances on probability and statistics (Eds. by Jiang, Y and Wang, G Z), Vol 1: 308-312.
8. Liu Y A and Xia Y M* (2010). Testing of Heteroscedasticity in Partially Linear Autoregressive Models with Exogenous Variables. The proceedings of 2010 international conference on probability and statistics of the international institute for general systems studies. Advances on probability and statistics (Eds. by Jiang, Y and Wang, G Z), Vol.1, 313-316.
9. Xia Y M* and Liu, Y A (2011). Robust asymptotic inferences for mean covariance model. Chinese Journal of applied probability and statistics, 27(4): 399-409.
10. Hui X F, Liu Y A, Xia Y M (2011).The Forest Area Forecast Based on the Ensemble Kalman Filtering. Forest Inventory and Planning, 36(5): 1-5.
11. Xia Y M* and Liu, Y A (2014). Simulation study, model selection and application for multivariate spatial latent variable model，Journal of Applied Statistics and Management, 33(5): 851-860.
12. Cheng Y S, Ding M W, Xia Y M, and Zhan W F (2014). Bayesian Analysis for dynamic generalized linear latent model with application to tree survival rate.Journal of Applied Mathematics,6:1-8.
13. Xia Y M* and Liu, Y A (2014). Robust Bayesian Analysis and Its Applications for Factor Analytic Model with Normal Scale Mixing. Chinese Journal of applied probability and statistics,30(4): 35-44.
14. Xia Y M*and Gou J W (2015). Assessing Heterogeneity of Multilevel Factor Analysis Model: A Semiparametic Approach, ACTA MATHEMATICAE APPLICATAE SINCA,38(4): 751-768.
15. Xia Y M*and Gou J W (2015). Estimation and Test for Dynamic Factor Analytic Model with Non-homogenous Structure,MATHEMATIC APPLICATA, 28(1): 65-73.
16. Xia Y M*, Gou J W and Liu Y A (2015). Semiparametric Bayesian Analysis for Factor Analysis Model Mixed with Hidden Markov Model, Applied Mathematics: A Journal of Chinese Universities (Ser.A) 2015,30(1): 17-30.
17. Xia Y M*and Gou J W (2016). Semi-parametric Bayesian Analysis for Latent Variable Models with Mixed Continuous and Ordinal Outcomes, Journal of Korean Statistics, 45(3): 451–465.
18. Xia Y M*, Tang N S and Gou J W (2016). Generalized Linear Latent Model for Multivariate Longitudinal Measurements Mixed with Hidden Markov Model. Journal of Multivariate analysis,152: 259-275.
19. Xia Y M* and Liu Y A (2016). Bayesian Semiparametric Analysis and Model Comparison for Confirmatory Factor Model,Chinese Journal of applied probability and statistics, 32(2): 157-183.
20. Xia Y M*(2016). Assessing Heterogeneity for Factor Analysis Model with Mixed Continuous and Ordinal Outcomes, Journal of Applied mathematics，421:1-12.
21. Xia Y M*, Chen G Y and Liu Y A (2016). Robust Inference on Hidden Markov Latent Variable Model. Journal of Systems Science and Mathematical sciences, 36(10):1783-1803.
22. Song X Y, Xia Y M and Zhu H T* (2017). Hidden Markov Latent Variable Models with Multivariate Longitudinal Data,Biometrics,73(1): 313-323.
23. Xia Y M* and Pan M L (2017). Bayesian Analysis for Confirmatory Factor Model with Finite Dimensional Dirichlet Prior Mixing. Commutation in Statistics: Simulation and Methods, 46(9): 4599-4619.
24. Xia Y M*, Gou J W (2017). A Note on Finite Dimensional Dirichlet prior. Commutation in Statistics: Simulation and Methods, 46(19): 9388-9396.
25. Xia Y M*, Zeng X Qand Tang N S (2018). Bayesian Analysis for Hidden Markov Factor Analysis Models with Application to Cocaine Use Data. New insights into Bayesian inference. Eds. by Nezhad M S F. IntechOpen:
26. Xia Y M*, Lin Y B and Xiong S C (2018). Bayesian Inference on Two-Part Model. ATHEMATICA APPLICATA, 31(4):761-778.
27. Xia Y M*(2019). Bayesian Inference on Two-Part Mixture Model. ATHEMATICA APPLICATA, 32(1):81-93.
28. Xia Y M* and Tang N S (2019). Bayesian Analysis for Mixture of Hidden Markov Latent Variable Models with Multivariate Longitudinal data. Computational Statistics and Data Analysis. 1(31): 741-765.
29. Xia Y M*, Lu B and Tang N S (2019). Inference on Two-part latent variable analysis model with multivariate longitudinal data. Structural equation modeling: A multidisciplinary Journal, 26(5):685-709.
30. Xia Y M*, Xiong S C and Tang N S (2019). Baysesian Semiparametric Analysis for Two-Part Latent Variable model. Communication in Mathematics and Statistics.DOI:10.1007/s40304-023-00359-1.
31. Xia Y M*, Liu Y A, Gou J W*(2022）Dirichlet Process and Its Developments: A Survey. Front. Math. China. 17(1): 79–115.
32. Xia Y M*, Zhu Q H. Gou J W (2022). Assessing Heterogeneity of Two-Part Model via Bayesian Model Clustering with Its Application to Cocaine Use Data. Data Clustering. August 17, Eds. by Tang N S. IntechOpen: London.
33. Gou J W, Xia Y M*(2022). Identification of Latent Variable Model with Multiple Categorical Variables.ATHEMATICA APPLICATA, 2:302-315
34. Gou J W, Xia Y M*, Jiang D P (2023). Bayesian Analysis of Two-Part Nonlinear Latent Variable Model:Semiparametric Method. Statistical modeling, 23(4): 376–399.
35. Liao X L, Chen J Y, Zhang Q. Xia Y M*(2023). Variational Bayesian Inference for Two-Part Latent Variable Models. Journal of Systems Science and Mathematical sciences, 43(14) :1039-1068.
36. Chen J Y, Lin Z Y, Xia Y M* (2023). Bayesian Analysis for Two-Part Latent Variable Model with Application to Fractional Data. Communication in Statistics: Theory and Methods. DOI:10.1080/03610926.2023.2273205.
37. Chen J Y, Yang C H, Xia Y M*(2023). Robust Model Fitting for Two-Part Model within Bayesian Semiparametric Framework: Variational Approach. REVSTAT-Statistical Journal. Accepted.
38. Yang C H, Liao X L, Xia Y M*(2024). Bayesian Inference on Generalized Cure Model. ATHEMATICA APPLI-CATA. 154:636-646.
39. Zhang Q; Zhang Y H; Xia* Y M (2024). Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations.Mathematics, 12(5), 783.
40. Xia Y M*, Chen J Y, Jiang D P (2024). Variational Bayesian analysis for general two-part latent variable model[J].
Computional Statistics, 39: 2259–2290.
41. Kang Q, Xia Y M*(2024). Bayesian Inference on Two-Part Model with Missing Covariates.
ATHEMATICA APPLICATA, accepted.
42.Xia Y M*, Wang H Y, Kang Q (2024). Variational Bayesian Inference on Two-Part Quantiles. Model. ATHEMATICA APPLICATA, accepted.
教材： 《概率论与数理统计》 刘应安,夏业茂， 科学出版社，2013.
