At a depth of 2890 km, the core-mantle boundary (CMB) separates turbulent flow of liquid metals in the outer core from slowly convecting, highly viscous mantle silicates. The CMB marks the most dramatic change in dynamic processes and material properties in our planet, and accurate images of the structure at or near the CMB--over large areas--are crucially important for our understanding of present day geodynamical processes and the thermo-chemical structure and history of the mantle and mantle-core system.
The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.
Type of Event:
Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence.
We analyze the mathematical foundations of three types of structured financial products: return optimization securities, yield magnet notes, and reverse exchangeable notes. These products were sold widely to retail "investors" in the mid-2000s. On the basis of their mathematical structure, we infer that these products could provide positive returns to a purchaser only if the stock market had continued on an enormous upward climb for most or all of the holding period.
We propose nonparametric estimators for the state occupation probabilities in a given state adjusting for the informative cluster size and one covariate at a time in a multistate model. This is a non-trivial problem since the state occupied is determined at a single inspection time for each subject and a group of subjects belongs to a cluster where cluster size is informative to their state status.
Many modern processes are capable of generating rich and complex data records not readily analyzed by traditional techniques. A single observation from a process might consist of n pairs of bivariate data that can be described via some functional relation (for example, a sequence of radar reflection signals measured over time). Or, each observation in a process may be a sample of data from some distribution. Methods are proposed here for detecting changes in such sequences from some known or estimated nominal state.
This talk provides an introduction to challenging statistical problems arising in the study of celestial objects: planets, stars, galaxies and the Universe as a whole. We start with a review of the close historical links between astronomy and statistics, from the ancient Greeks through Laplace and Gauss. However, the communities diverged during the 20th century, developing into a poor state with great needs for advanced methodology but weak links between the fields. This is ameliorating today with a vibrant subfield of astrostatistics.
Gas hydrate, which is essentially methane in ice, is a potentially important worldwide energy resource. There is evidence of significant in-place gas hydrate resources in offshore deepwater areas of the world. Southwest Statistical Consulting under grants from U.S Bureau of Ocean Energy Management (BOEM) is developing a mass-balance cell-based model using stochastic simulation to obtain estimates of in-place gas hydrate resources in U.S. Federal off-shore areas.
Asset pricing and volatility modeling take a center stage in financial econometrics. This talk introduces a new method that helps calibration of stochastic volatility models via Markov chain Monte Carlo (MCMC) Bayesian inference based on returns and option data. With the presence of high-dimensional latent volatility processes, numerical integration for computing option prices is required at every time point and every iteration of MCMC.
Sufficient dimension reduction (SDR) ideas are used for supervised dimension reduction in regression problems. Support Vector Machine (SVM) algorithms belong to the class of machine learning techniques which are used for classification. In this talk we discuss Principal Support Vector Machine (PSVM) a method which utilizes SVM to achieve sufficient dimension reduction. PSVM has several advantages over existing methodology for sufficient dimension reduction, with the most important one being the fact that we can do linear and nonlinear dimension reduction under a unified framework.
Discrete renewal processes are ubiquitous in stochastic phenomenon. In this talk constructing a discrete process where renewals are more (or less) likely during specified seasons is of specific interest. For example thunderstorms in the Southern United States can take place at any time in the year, but are most likely during the summer. Hurricanes, tornadoes, and snowstorms are other meteorological count processes obeying periodic dynamics. Rare disease occurrences, accidental deaths, and animal sightings are non-meteorological examples of count phenomenon following a periodic structure.