Home > Events > SMAC Talks > Stochastic Modeling and Computational Statistics, Fall 2014

Stochastic Modeling and Computational Statistics, Fall 2014

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  1. 40 minutes for each talk + 20 minutes for discussion.
  2. The talk should be accessible to all grad students who have completed 1 year of the program.
  3. Informal style. For instance, chalk and blackboard talks are welcome.
  4. Interruptions during the talk are welcome but they should only be for clarifications; longer questions are to be left to the discussion period.
  5. Unpublished work may not be shared or discussed outside the group without the permission of the speaker/author.
  6. While a large proportion of the talks may be related to stochastic modeling and computing, a much broader list of topics have also been discussed in this series.



August 29 Runze Li A High-Dimensional Nonparametric Multivariate Test for Mean Vector
September 5 seminar cancelled
September 12 Kwame Kankam Robust Parameter Design: A Penalized Likelihood Approach
September 19 Mark Roberts (Dept of Economics, PSU) The new Penn State RDC
September 26 Michael Akritas Projection Pursuit Multiple Index (PPMI) Models
October 3 Naomi Altman Generalizing Principal Components Analysis
October 10 Matthew Reimherr On prior specification for Bayesian inference
October 17 Kari Lock Morgan Balancing Covariates via Propensity Score Weighting
October 24 No seminar (Chris Wikle's talks in stats and meteorology this week)
October 31 Youngjoo Cho Covariate adjustment using propensity scores for dependent censoring problems
November 7 Zita Oravecz (Human Development and Family Studies, PSU) Insights into psychometric process modeling in the hierarchical Bayesian framework
November 14 Peter Molenaar (Human Development and Family Studies, PSU) Equivalent dynamic models and the consequences for Granger causality testing
November 21 No talk (day before Thanksgiving break)
November 28 Thanksgiving break
December 5 Eugene Morgan (Dept of Energy and Mineral Engineering, PSU) Characterizing subsurface gas through Bayesian inversion of a seismic attenuation model