Hanxiang Peng

Research

Areas of current interests: (1) Survival analysis: I explore hazards regression using free knot splines and study its asymptotic behaviors with applications in health sciences. (2) Modeling of correlated data and generalized linear models: I exploit exchangeability and partial exchangeability to model correlated data common biological and medical sciences. (3) Semi-parametric regression: I explore asymptotic efficiency in semi-parametric models. (4) Robust statistics: I explore robust regression with depth functions. (5) Empirical likelihood: I explore empirical likelihood with infinitely many constraints, maximum empirical likelihood estimation and semiparametric efficiency. Variable selection under empirical likelihood loss.

Publications & Professional Activity

Publications as a senior or lead author are indicated with an asterisk

Hanxiang Peng and Anton Schick (2012)
An Empirical Likelihood Approach of Goodness of Fit Testing
Appear in Bernoulli

Hanxiang Peng, Xin Dang, and Xueqin Wang. 
The Partially Exchangeable Distribution. 
Statist. Probabil. Lett. 80, 932-938 (2010). DOI: 10.1016/j.spl.2010.02.003 *

Fei Tan, Gibson Johnston Rayner,  Xiaodong Wang and Hanxiang Peng. 
A Full Likelihood Procedure of Exchangeable Negative Binomials for Modelling Correlated and Overdispersed Count Data.
J. Statist. Plann. Inferr., (2010). DOI: 10.1016/j.jspi.2010.03.008 *

Hanxiang Peng and Anton Schick. 
Improving Efficient Marginal Estimators in Bivariate Models with Parametric Marginals.
Statist. Probabil. Lett. 79, 2437-2442 (2009).

Xin Dang, Stephine L. Keeton, Hanxiang Peng.
A Unified Approach for Analyzing Exchangeable Binary Data with Applications to Clinical and Developmental Toxicity Studies.
Statist. Med. 28: 2580-2604 (2009). DOI: 10.1002/sim.3638. *

Yixin Chen, Xin Dang, Hanxiang Peng. 
Outlier Detection: A Novel Depth Approach. 
Book Chapter: Dynamic and Advanced Data Mining for Progressing Technological Development, published by IGI Global
To appear in 2009.

Yixin Chen, Xin Dang, Hanxiang Peng, and Henry L. Bart, Jr. 
Outlier Detection with the Kernelized Spatial Depth Function. 
IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 288 - 305 (2009). 

Hanxiang Peng and Xueqin Wang. 
Moment estimation in a semiparametric generalized linear model.
Statist. Probabil. Lett. 138: 1836-1850 (2008).* 

Hanxiang Peng. 
Efficient inference in a semiparametric generalized linear model.
J. Nonparametric Statist. 20(2): 115 -127 (2008). * 

H.Peng, Shaoli Wang and X. Wang.
Consistency and Asymptotic Distribution of the Theil-Sen Estimator.
J. Statist. Plann. Inferr. 138(6):1836-1850 (2008). *

Y. Ding, X. Dang, H. Peng and D. Wilkin.
Robust Clustering in High Dimensional Data Using Statistical Depths. 
Bioinformatics (BMCBI) 8(S-7) (2007). * 

Henry Bart, Yixin Chen, Xin Dang, and Hanxiang Peng.
Depth-Based Novelty Detection and its Application to Taxonomic Research. Accepted as a regular paper by the Seventh IEEE International Conference on Data Mining, Omaha, Nebraska, USA, Oct 28-31, 2007. 
Published in the conference proceedings by the IEEE Computer Society Press.

H. Peng and A. Schick.
Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown, marginals: The least squares approach. 
J. Mult. Analy. 95(2) :385-409 (2005). 

H. Peng and A. Schick.
Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown, marginals: The minimum chi-square approach. 
Stat. & Deci. 22: 301-318 (2004). 

H. Peng and A. Schick. 
Estimation of linear functionals of bivariate distributions with parametric marginals.
Stat. & Deci. 22: 61-77 (2004).

S. Penev, H. Peng, A. Schick and W. Wefelmeyer. 
Efficient estimators for functionals of Markov chains with parametric marginals. 
Statist. & Probabil. Lett. 66: 335-345 (2004) . 

J. Forrester, W. Hooper, H. Peng and A. Schick. 
On the construction of efficient estimators in semiparametric models. 
Stat. & Deci. 21: 109-138 (2003).

H. Peng and A. Schick. 
On efficient estimation of linear functionals of a bivariate distribution with known marginals.
Statist. & Probabil. Lett. 59: 83-91.(2002).

Affiliations

Statistics Consulting Center