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Shijia Wang

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I am a PhD candidate in Statistics at Simon Fraser University. My research focuses on computational statistics and statistical machine learning. I am particularly interested in Bayesian nonparametric modelling and Monte Carlo methods such as Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC). I am also interested in Mixed effects model and fishery management reference points.

Education

Simon Fraser University2015-present

PhD Candidate in Statistics
Supervisor: Liangliang Wang

Memorial University of Newfoundland2013-2015

MSc in Statistics (Fellow of the School of Graduate Studies)
Supervisor: Noel Cadigan

Publications

Wang, S., Cadigan, N. G., & Benoit, H. P. (2016). Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements. Journal of Applied Statistics. (In press)

Cadigan, N. G., & Wang, S. (2016). Local sensitivity of per recruit fishing mortality reference points. Journal of Biological Dynamics. (In press)

Awards & Achievement
  • CD Nelson Memorial Graduate Entrance Scholarship, SFU
  • Provost Prize of Distinction, SFU
  • Special Graduate Entrance Scholarship, SFU
  • Fellow of the school of Graduate Studies, MUN
  • The title of Fellow of the School of Graduate Studies is awarded in recognition of outstanding academic achievement throughout a graduate program.
  • Student Travel award of Statistical Society of Canada, 2015
  • SGS Fellowship, MUN
Presentations & Posters
  • Pattern Discovery of Health Curves with an Ordered Probit Model and Functional PCA, JSM 2016
  • (poster) Ordinal healthcare data analysis using Bayesian smoothing and functional PCA, SFU Health Research Day 2016
  • Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements, SSC 2015
  • Local sensitivity of per recruit fishing mortality reference points, Centre for Fisheries Ecosystems Research Research Advisory Committee Meeting, 2015
  • Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements, Department Seminar of MUN, 2014