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Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 27th September 2016
Dr Pavel Krupskii, CEMSE Division, King Abdullah University of Science and Technology
Factor Copula Models for Replicated Spatial Data
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We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matern model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 20th September 2016
Dr. Guangyu Zhu, UBC Statistics Postdoctoral Research Fellow
Sparse Envelop Model: Efficient Estimation and Response Variable Selection in Multivariate Linear RegressionTitle
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Response variable selection arises naturally in many applications, but has not been studied as thoroughly as predictor variable selection. In this talk, I will firstly introduce the envelope model which allows efficient estimation in multivariate linear regression. Then I will discuss response variable selection in both the standard multivariate linear regression and the envelope contexts. Finally I will introduce the sparse envelope model we proposed to perform variable selection on the responses and preserve the efficiency gains offered by the envelope model. We establish consistency and the oracle property and obtain the asymptotic distribution of the sparse envelope estimator.

Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 8th September 2016
Bayesian data science
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Two arguments for not using Bayesian statistics in a data science context are:
1. Having to wait for several hours of MCMC simulation every time you fix a bug in your model.
2. Worse: having to spend several months implementing finicky MCMC algorithms.
I will talk about the work of my collaborators, students, and myself on trying to make this suffering less severe, using ideas from the disparate fields of statistical mechanics and software engineering.
**Warning:** This will mostly be an unconventional talk: a large chunk will be devoted to introducing "blang", an experimental probabilistic programming language for Bayesian data science we are working on.  **Please bring your laptop with Chrome installed.**

Please also let me know ( if you are interested in staying after the talk for continuing with pizza and a more hands-on primer to declarative Bayesian data science with blang.



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