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副教授
- 电子邮箱:0cfde6a93bebfbf93a81f2836b5cbc62564ca52797af4e34099290e0e3e130a5d3eb11f5a9cbc81dcbe6c5dc03b92898ed32bd59b2a1a20f55c4e3da2756dd12a15fe36c60375866888a72951ce50a49f96a477d9705c6ed72c9c6e4726eb9caf5e02692726e729b9cf3ca04306ff6bfa034d10bceaf388d66adf9626c068b53
- 联系方式:+86-551-63600560
- 学位:博士
访问量:
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[41]Wang, Z.,Wu, Y. and Zhao, L. (2009). Approximation by randomly weighting method for linear hypothesis testing in censored regression model. Science in China Series A: Mathematics 52, 561-576..
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[42]Wu, C., Wang, Z., Liu, L., Zhao, P.,Wang,W., Yao, D., Shi, B., Lu, J., Liao, P., Yang, Y. and Zhu, L. (2009). Surface Enhanced Laser Desorption/Ionization profiling: a new diagnostic method of HBV related hepatocellular carcinoma. Journal of Gastroenterology and Hepatology 24, 55-62..
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[43]Wu, Y., Liu, C. andWang, Z. (2008). Large sample properties of random weighted estimators for parametric component in semi-parametric regression models. Journal of University of Science and Technology of China, 38, 05-0496-09..
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[44]Gao, Q., Wu, Y., Zhu, C. and Wang, Z. (2008). Asymptotic normality of maximum quasi-likelihood estimators in generalized linear models with fixed design. Journal of Systems Science and Complexity,21, 463-473..
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[45]Cao, G., Wang, Z.,Wu, Y. and Zhao, L. (2008). Inference of Change-Point in Single Index Models. Science in China Series A: Mathematics 51, 1855-1870..
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[46]Wang, Z.,Wu, Y. and Zhao, L. (2008). Nonparametric Inference in a Simple Change-point Model. ACTA Mathematica Sinica-English Series 24, 1483-1496..
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[47]Wang, Z., Chang, Y., Ying, Z., Zhu, L. and Yang, Y. (2007). A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve. Bioinformatics 23, 2788-2794..
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[48]Wang, Z.,Wu, Y. and Zhao, L. (2007). Change-point estimation for censored regression model. Science in China Series A: Mathematics 50, 63-72..
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[49]王占锋,王占锋.Extended t-process regression models.JOURNAL OF STATISTICAL PLANNING AND INFERENCE,2017,18938-60.
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[50]王占锋,王占锋.Sequential estimate for linear regression models with uncertain number
of effective variables.METRIKA,2013,76(7):949-978.