中文
Published date:2014-03-14    Provided by:数学系
Personal Information
Name: Wang Lichun
Sex: Men
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E-mail:  
Present Address: Associate Professor, Ph.D. Department of Mathematics, Beijing Jiaotong University Beijing , P.R. China
Post Card: 100044
Education
Ph.D. Dept. of Statistics and Finance, USTC, China,1999/7-2002/6
Major: Mathematical Statistics
Research Interest
Bayes and empirical Bayes theory, censored data, asymptotic theory, regression model. 2005.1-2007.12.
Funding
 
Major Publication

[1] L.C.Wang, Noel Veraverbeke (2009), Bayes prediction based on right censored data, Statistical Papers, Springer,50:137-149.
[2] L.C.Wang, X. Wang (2009), The life-span prediction of a system connected in series, Mathematics and Computers in Simulation, Elsevier, 79:1770-1777.
[3] L.C.Wang (2008), Life prediction under random censorship,Journal of Statistical Computation and Simulation, 78:11,1033-1044.
[4] L.C.Wang, {N. Veraverbeke} (2008), MSE superiority of Bayes and empirical Bayes estimators in two generalized seemingly unrelated regressions, Statistics & Probability Letters,78, 109-117.
[5] L.C.Wang (2007), Monotone Empirical Bayes test for scale parameter under random censorship, Chinese Journal of Applied Probability and Statistics, 4, 419-427.
[6] L.C.Wang, N. Veraverbeke (2006), Empirical likelihood in a semi-parameter model for missing response data, Communications in Statistics-Theory and Methods, 35, 625-639.
[7] L.C.Wang, Q. H. Wang (2006), Empirical likelihood for parametric model under imputation for missing data, Journal of Statistics & Management Systems, 9, 1-13.
[8] L.C.Wang (2005), Bayes and empirical Bayes tests for the life parameter, Applicationes Mathematicae (Poland), 2,133-143.
[9] L.C.Wang (2005), Bayes and empirical Bayes iteration estimators in two seemingly unrelated regression equations, Sciences in China, Series A, 9, 1153-1168.
[10] L.C.Wang (2005), Convergence rates of empirical Bayes estimation for parameter of the uniform distribution U(0, $/theta$) under random censorship, Communications in Statistics-Theory and Methods, 34, 2209-2220.


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