Skiles 206 School of Mathematics Georgia Institute of Technology Atlanta, GA 30332-0160
Education:
Ph.D. in probability and mathematical statistics,
Erasmus University Rotterdam . Supervisor: Professor Laurens de Haan.
Time: Nov. 1, 1993 -- June 25, 1998. Thesis title:
Second Order Condition and Extreme Value Theory , Tinbergen Institute Research
Series 178, Thesis Publisher, Amsterdam, 1998.
M.S. in probability, Peking University. Supervisor: Professor Shihong Cheng.
Time: Sep. 1, 1990 -- July 1, 1993.
B.S. in mathematics, Zhejiang University. Time: Sep. 1, 1986 -- July 1, 1990.
August 1, 2000 -- December 31, 2000: Visiting assistant
professor at the Department of Mathematics , The Hong Kong University of Science and Technology, Hong Kong.
Sep. 1, 1998 -- July 31, 2000: Postdoctoral research
fellow under the supervision of Professor Peter Hall at the
Center
for Mathematics and
its Applications, Australian National University.
Nov. 1, 1993 -- June 25, 1998: Research assistant at Tinbergen Institute,
Erasmus University Rotterdam.
Extreme value theory in finance and environmental sciences
Nonparametric statistics
Heavy tailed, long-range dependent and nonlinear time series
Empirical likelihood methods
Copula and tail copula in risk management
Continuous-time stochastic processes in finance
Selected papers:
[43] Lu-Hung Chen, Ming-Yen Cheng and Liang Peng (2008). Conditional variance estimation in heteroscedastic regression model. JSPI. Accepted.
[42] L. Peng (2008). Estimating the probability of a rare event via elliptical copulas. NAAJ. Accepted.
[41] J. Chen, L. Peng and Y. Zhao (2008). Empirical likelihood based confidence intervals for copulas. JMVA. Accepted.
[40] Alex J. Koning and Liang Peng (2008). Goodness-of-fit tests for a heavy tailed distribution. JSPI. Accepted.
[39] Claudia Kluppelberg, Gabriel Kuhn and Liang Peng (2008). Semi-Parametric Models for the Multivariate Tail Dependence Function - the Asymptotically Dependent Case. SJS. Accepted.
[38] Liang Peng and Yongcheng Qi (2007). Bootstrap Approximation of Tail Dependence Function. JMVA. Accepted.
[37] Ngai-Hang Chan, Liang Peng and Dabao Zhang (2007).
Empirical likelihood based confidence intervals for conditional variance in heteroscedastic regression models.
Econometric Theory. Accepted.
[36] L. de Haan, C. Neves and L. Peng (2008). Parametric tail copula estimation and model testing. JMVA 99, 1260 - 1275.
[35] N.H. Chan, J. Chen, X. Chen, Y. Fan and L. Peng (2007). Statistical inference for multivariate residual copula of GARCH models. Statistica Sinica. Accepted.
[34] Dabao Zhang, Martin T. Wells and Liang Peng (2008). Nonparametric estimation of the dependence function for a multivariate extreme value distribution. JMVA 99(4), 577 - 588.
[33] Claudia Kluppelberg, Gabriel Kuhn and Liang Peng (2007). Estimating the tail dependence of an elliptical distribution.
Bernoulli 13(1), 229 - 251.
[32] M. Cheng, L. Peng and J.S. Wu (2007). Reducing variance in univariate smoothing. Ann. Statist. 35(2), 522 - 542.
[31] L. Peng and Y. Qi (2007). Partial derivatives and confidence intervals of bivariate tail dependence functions. Journal of Statistical Planning and Inference 137, 2089 - 2101.
[30] M. Cheng and L. Peng (2007). Variance reduction in multivariate likelihood models. JASA. 102(477), 293 - 304.
[29] Ngai Hang Chan, Shijie Deng, Liang Peng and Zhendong Xia (2007). Interval estimation for the conditional Value-at-Risk
based on GARCH models with heavy tailed innovations. Journal of Econometrics 137(2), 556 - 576.
[28] L. Peng and Y. Qi (2006). Confidence regions for high quantiles of a heavy tailed distribution. Annals of Statistics 34(4), 1964 - 1986.
[27] G.T. Zhou and L. Peng (2006). Optimality condition for selected mapping in OFDM.
IEEE Transactions on Signal Processing 54(8), 3159 - 3165.
[26] M. Cheng and L. Peng (2006). A simple and efficient improvement of multivariate local linear regression.
Journal of Multivariate Analysis 97(7), 1501 - 1524.
[25] N.H. Chan, L. Peng and Y. Qi (2006). Quantile inference for near-integrated autoregressive time series with infinite
variance. Statistica Sinica 16(1), 15 - 28.
[24] Liang Peng and Yongcheng Qi (2006). A new calibration method of constructing empirical likelihood-based confidence intervals for the tail index. Australian & New Zealand Journal of Statistics 48(1), 59 - 66.
[23] Ngai
Hang Chan and Liang Peng (2005). Weighted least absolute deviations estimation for an AR(1) process
with ARCH(1) errors. Biometrika. 92, 477 - 484.
[22] Liang Peng and Yongcheng Qi (2004). Estimating the first and second order parameters
of a heavy tailed distribution. Australian & New Zealand Journal of Statistics. 46(2), 305 - 312.
[21] Shiqing Ling and Liang Peng (2004). Hill's estimator for the tail index of an ARMA model.
Journal of Statistical Planning and Inference. 123(2), 279 - 293.
[20] Liang Peng and Qiwei Yao (2004). Nonparametric regression under dependent errors with infinite variance.
Ann. Inst. Statist. Math. 56(1), 73 - 86.
[19] Liang Peng (2004). Bias-corrected estimators for monotone and concave
frontier functions. Journal of Statistical Planning and Inference. 119(2), 263 - 275.
[18] Liang Peng (2004). Empirical likelihood based confidence interval for the mean of a heavy tailed distribution.
Annals of Statistics. 32(3), 1192 - 1214.
[17] Liang Peng and Xiaohua Zhou (2004). Local linear smoothing of receiver operator
characteristic (ROC) curves. Journal of Statistical Planning and Inference. 118, 129 - 143.
[16] L. Peng and Q. Yao (2003). Least absolute deviations estimation for ARCH and GARCH models.
Biometrika. 90(4). 967 - 975.
[15] G. Claeskens, B. Jing, L. Peng and W. Zhou (2003). Empirical likelihood confidence
regions for comparison distributions and ROC curves. The Canadian Journal of Statistics.
31(2), 173 - 190.
[14] A. Ferreira, L. de Haan and L. Peng (2003). On optimising the estimation of high quantiles of a probability distribution.
Statistics. 37(5), 403-434.
[13] Ming-Yen Cheng and Liang Peng (2002). Regression modeling for nonparametric estimation of distribution
and quantile functions. Statistica Sinica, 12, 1043 - 1060.
[12] Peter Hall, Liang Peng and Qiwei Yao (2002). Moving-maximum models for extremes of time series.
Journal of Statistical Planning and Inference, 103, 51 - 63.
[11] Peter Hall, Liang Peng and Qiwei Yao (2002). Prediction and nonparametric estimation for time series with heavy tails.
Journal of Time Series Analysis. 23(3), 313 - 331.
[10] Peter Hall, Liang Peng and Nader Tajvidi (2002). Effect of extrapolation on coverage accuracy of prediction intervals
computed from Pareto-type data. Annals of Statistics, 30(3), 875 - 895.
[9] Shihong Cheng and Liang Peng (2001). Confidence intervals for the tail index.
Bernoulli. 7(5), 751 - 760.
[8] Peter Hall, Liang Peng and Christian Rau (2001). Local-likelihood tracking of fault lines and boundaries in spatial problems.
JRSSB, 63(3), 569 - 582.
[7] Jon Danielsson, Laurens de Haan, Liang Peng and Capser G. de Vries (2001). Using a bootstrap
method to choose the sample fraction in tail index estimation.
Journal of Multivariate Analysis 76, 226 - 248.
[6] Jaap Geluk, Liang Peng and Casper G. de Vries (2000). Convolutions of heavy tailed random variables and applications to
portfolio diversification and MA(1) time series.
Advances in Applied Probability, 32(4), 1011 - 1026.
[5] Shihong Cheng, Liang Peng and Yongcheng Qi (2000). Ergodic behaviour of extreme values.
J. Austral. Math. Soc. (Series A) , 68 , 170 -- 180.
[4] Peter Hall, Liang Peng and Nader Tajvidi (1999). On prediction intervals based on predictive
likelihood or bootstrap methods.
Biometrika , 86 , 871 -- 880.
[3] Laurens de Haan and Liang Peng (1999). Exact rates of convergence to a stable law.
Journal of the London Mathematical Society , 59(2) , 1134 -- 1152.
[2] Laurens de Haan and Liang Peng (1998). Comparison of tail index estimators.
Statistica Neerlandica , 52(1) , 60 -- 70.
[1] Laurens de Haan and Liang Peng (1997). Rates of convergence for bivariate extremes.
Journal of Multivariate Analysis , 61(2) , 195 -- 230.
Technical reports:
Liang Peng, Yongcheng Qi and Chen Zhou (2007). Maximum likelihood estimation of extreme value index for irregular cases.
MLE.pdf
Deyuan Li and Liang Peng (2008). Still fit generalized Pareto distributions?
GPD.pdf
Liang Peng (2007). A practical way for analyzing heavy tailed data.
HeavyTail.pdf
Liang Peng (2007). A practical way for estimating tail dependence functions.
Dependence.pdf
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are his views and opinions. They do not necessarily represent those of the Georgia Institute of Technology
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