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Hao Zhang

Forestry and Natural Resources 

  • Professor of Statistics and Natural Resources
  • Prof of Statistics/Prof of FNR
765.496.9548
MATH Room 536
150 N. University Building
West Lafayette, IN 47907

I am Professor of Statistics and Professor of Forestry and Natural Resources, and have a split appointment with Department of Statistics (75%) and Department of Forestry and Natural Resources (25%). This joint appointment facilitates interdisciplinary research and provides me access to various applications in forestry, agriculture and natural resources. Those applications in turn shape my methodological research in statistics. I strongly believe that methodological research and applications mutually enhance each other.

My current research is primarily in the analysis of spatial and space-time data. These kinds of data are being observed in many fields such as climatology, geophysics, geology, natural resources, agriculture, health sciences, economics and marketing. Technological advances have made it feasible to collect and archive space-time data at large scales that were not possible in just a decade ago. These massive and correlated data create challenging and interesting statistical problems, and demand innovative computational and methodological research.

Awards & Honors

(2009) Elected to a member of the International Statistical Institute. International Statistical Institute.

Selected Publications

Zhang, H., Du, J., & Mandrekar, V. (2009). Fixed-domain asymptotic properties off tapered maximum likelihood estimators. Annals of Statistics, 37, 3330-3361.

Zhang, H., Mateu, J., & Porcu, E. (in press). On some weighted composite likelihood-based tests for space-time separability of covariance functions. Computational Statistics and Data Analysis.

Zhang, H. (in press). On spatial skew-Gaussian processes and applications. Environmetrics.

Zhang, H., & Wang, Y. (in press). Kriging and cross-validation for massive spatial data. Environmetrics.

Zhang, H., Johnson, D., DasGupta, N., & Evans, M. (2009). Internet Approach versus Lecture and Lab-Based Approach for Teaching an Introductory Statistical Methods Course: Students’ Opinions. Teaching Statistics, 31.