The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.

An Integrative Statistical Framework for Multi-Modal Omics Data

It is becoming increasingly common for patients to be profiled across multiple molecular compartments -genomic, transcriptomic, proteomic, metab​olomic, etc. We develop a framework that leverages recent developments in the estimation of high-dimensional multi-layered graphical models that provide insights on regulatory mechanisms across molecular compartments (layers), as well as on molecular interactions within each layer and are also capable of accommodating outcome variables such as disease risk, or patient survival times.

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Online Updating of Computer Model Output Using Real-Time Sensor Data

Data center thermal management has become increasingly important because of massive computational demand in information technology. To advance the understanding of the thermal environment in a data center, complex computer models are extensively used to simulate temperature distribution maps. However, due to management policies and time constraints, it is not practical to execute such models in a real time fashion.

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Modeling Spatiotemporal Precipitation Variability and Glacier Mass Balance in High Mountain Asia

With the exception of the earth's polar regions, the High Mountain Asia region (including the Tibetan Plateau) contains more of the world's perennial glaciers than any other. Sometimes called the "third pole" because of its massive storage of ice, High Mountain Asia (HMA) provides water to one-fth of the world's population. Due to changes in precipitation patterns and temperatures warming faster in HMA than the global average, the region faces increased risk of flooding, crop damage, mudslides, economic instability, and long-term water shortages for the communities down-river.

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Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data

We introduce a computationally efficient Bayesian model for predicting high-dimensional dependent count-valued data. In this setting, the Poisson data model with a latent Gaussian process model has become the de facto model. However, this model can be difficult to use in high dimensional settings, where the data may be tabulated over different variables, geographic regions, and times. These computational difficulties are further exacerbated by acknowledging that count-valued data are naturally non-Gaussian.

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Partitioning a Large Simulation as It Runs

As computer simulations continue to grow in size and complexity, they present a particularly challenging class of big data problems. Many application areas are moving toward exascale computing systems, systems that perform a billion billion FLOPS (FLoating-point Operations Per Second). Simulations at this scale can generate output that exceeds both the storage capacity and the bandwidth available for transfer to storage, making post-processing and analysis challenging.

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Some methods for functional econometric time series

The talk will introduce fundamental ideas of the analysis of time series of functions. Examples of such time series are yield curves and intraday return curves. Within the framework of functional data analysis, fundamental concept of long-run covariance, autocorrelations and their estimators will be introduced. Three specific inferential problems will be discussed: (1) testing if a functional time series is stationary, (2) testing if it is a functional weak white noise, (3) detecting change points in its mean structure in the presence of changing variability.

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High-dimensional Bayesian Geostatistics

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatial-temporal process models have become widely deployed statistical tools for researchers to better understanding the complex nature of spatial and temporal variability.

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The Big Data Issues in Spatial Statistics

One of areas where big data are collected is in environmental and climate studies. The Global Circulation Models or Regional Circulation Models can generate huge amount of data in space and time. Data collected through remote sensing or sensor networks are also huge. All these data are correlated spatially and temporally. One therefore has to deal with the huge covariance matrix in the traditional likelihood-based inferences or Bayesian inferences.

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Beyond Analysis: Teaching Statistics with Modern Statistical Software

Modern statistical software isn’t just a tool to help students analyze data, but through interactive graphics and rich statistical visualization these tools help students learn and engage with core concepts in statistics and data analysis. In this session we will see examples of how to use interactivity of software to aid in the communication of otherwise difficult to grasp concepts in the analysis and visualization of data.

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Space and circular time log Gaussian Cox processes with application to crime event data

We view the locations and times of a collection of crime events as a space-time point pattern. So, with either a nonhomogeneous Poisson process or with a more general Cox process, we need to specify a space-time intensity. For the latter, we need a random intensity which we model as a realization of a spatio-temporal log Gaussian process. In fact, we view time as circular, necessitating valid separable and nonseparable covariance functions over a bounded spatial region crossed with circular time. In addition, crimes are classified by crime type.

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