Seminar Schedule in Google Calendar
Tue 21st October 2014
Dr. Michael Messer, PhD in Mathematics (Goethe University, Frankfurt, Germany)
Dr. Michael Messer completed his doctorate in Mathematics with
specialization in Statistics at the department of Computer Science and
Mathematics at Goethe University Frankfurt, Germany.
His thesis was supervised by Prof. Gaby Schneider and reported by Prof.
Anton Wakolbinger (both Frankfurt) and Prof. Roland Fried from Dortmund.
The research was conducted in collaboration with neuroscientists in an
interdisciplinary research project for the investigation of neuronal
disorders (NeFF - Neuronal Coordination Research Focus Frankfurt).
He developed statistical techniques for the analysis of neuronal spike trains.
The main paper was recently accepted for publication at The Annals of Applied Statistics.
Detecting Rate Changes in Point Processes
Nonstationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train recordings, we define a general class of renewal processes. This class is used to test the null hypothesis of stationary rate versus a wide alternative of renewal processes with nitely many rate changes (change points). Our test extends ideas from the ltered derivative approach by using multiple moving windows simultaneously. We also develop a multiple lter algorithm, which can be used when the null hypothesis is rejected in order to estimate the number and location of change points. We analyze the benefits of multiple ltering and its increased detection probability as compared to a single window approach. Application to spike trains recorded from dopamine midbrain neurons of anesthetized mice illustrates the relevance of the proposed techniques as preprocessing steps for methods that assume rate stationarity.
Michael Smith Labs Room 102
Thu 16th October 2014
Reconstructing carbon dioxide for the last 2000 years: a hierarchical success story
Knowledge of atmospheric carbon dioxide (CO2) concentrations in the past are important to provide
an understanding of how the Earth's carbon cycle varies over time. This project combines ice core
CO2 concentrations, from Law Dome, Antarctica and a physically based forward model to infer CO2
concentrations on an annual basis. Here the forward model connects concentrations at given time
to their depth in the ice core sample and an interesting feature of this analysis is a more
complete characterization of the uncertainty in "inverting" this relationship. In particular,
Monte Carlo based ensembles are particularly useful for assessing the size of the decrease in CO2
around 1600 AD. This reconstruction problem,also known as an inverse problem, is used to illustrate
a general statistical approach where observational information is limited and characterizing the
uncertainty in the results is important. These methods, known as Bayesian hierarchical models,
have become a mainstay of data analysis for complex problems and have wide application in the
This work is in collaboration with Eugene Wahl (NOAA), David Anderson (NOAA) and Catherine
Michael Smith Labs Room 102
Tue 14th October 2014
Multi-resolution spatial methods for large data sets (joint EOS,SCAIM,PIMS,STAT)
Spatial data is ubiquitous arising in numerous areas in the geophysical and environmental
sciences. A basic problem for statisticians is to estimate complete surfaces from irregular
observations or measurements and to quantify the uncertainty in the result. However, standard
statistical methods break when applied to large data sets and so alternative approaches are
needed that balance shortcuts in the statistical models for increases in computational
efficiency. A useful method expands the surface in a set of compact basis functions and places
a Markov random field model on the basis coefficients. The impact is that evaluating the model
likelihood and computing spatial predictions is feasible even for tens of thousands of spatial
observations on a single computational core (e.g. a laptop). Moreover, by varying the support
of the basis functions and the correlations among basis coefficients it is possible to entertain
multi-resolution and non-stationary spatial models that mirror the rich covariance structure often
found in large geophysical data sets.
See: A multi-resolution Gaussian process model for the analysis of large spatial data sets.
D Nychka, S Bandyopadhyay, D Hammerling, F Lindgren, S Sain (2014) Journal of Computational and
Graphical Statistics (In press).
Tue 7th October 2014
Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling
Bayesian additive regression trees (BART) have become increasingly popular as flexible and scalable non-parametric models useful in many modern applied statistics regression problems. They bring many advantages to the practitioner dealing with large datasets and complex non-linear response surfaces, such as the matrix-free formulation and the lack of a requirement to specify a regression basis a priori. However, there are some known challenges to this modeling approach, such as poor mixing of the MCMC sampler and inappropriate uncertainty intervals when the assumed homoscedastic variance model is violated. In this talk, weintroduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear
to adequately explore the posterior tree space of this model.