Attention conservation notice: Self-promotion, based on unpublished papers.
- At the Columbia University Applied
Math department: "LICORS: Light Cone Reconstruction of States for
Non-parametric Forecasting of Spatiotemporal Processes"
- Abstract: We present a new, non-parametric forecasting method for data where continuous values are observed discretely in space and time. Our method, "light-cone reconstruction of states" (LICORS), uses physical principles to identify predictive states which are local properties of the system, both in space and time. LICORS discovers the number of predictive states and their predictive distributions automatically, and consistently, under mild assumptions on the data source. This leads to a natural measure of local predictive complexity, which can be used for automatic pattern discovery. Our work provides applied researchers with a new, highly automatic method to analyze and forecast spatio-temporal data. (Joint work with Georg M. Goerg; arxiv:1206.2398)
- Time and place: 10--11 am on Thursday, 18 October 2012, in 214 Mudd Hall
- At the NYU Stern School Statistics Research Seminar, "Consistency under Sampling of Exponential Random Graph Models"
- Abstract: The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling, or, in terms of the theory of stochastic processes, that it defines a projective family. Focussing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power. These results are actually special cases of more general ones about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses. (Joint work with Alessandro Rinaldo; arxiv:1111.3054)
- Time and place: 11:30--12:30 on Friday, 19 October 2012, in 5-80 Kaufmann Management Center (KMC)
Both talks are free, but I don't know if you'd have to show university ID to
get into the buildings.
"LICORS" is pronounced "liquors". "Consistency under Sampling of
Exponential Random Graph Models" is pronounced "Really, all we wanted to do was prove that maximum likelihood works when the data is a big
Posted at October 16, 2012 15:08 | permanent link