Prof. Victor DeOliveira, Professor in Department of Management Science and Statistics in the Carlos Alvarez College of Business
Wednesday, March 15, 2023, 15:00
- 16:00
Building 1, Level 4, Room 4102
The Mat\'ern family of covariance functions is currently the most commonly used for the analysis of geostatistical data due to its ability to describe different smoothness behaviors. Yet, in many applications the smoothness parameter is set at an arbitrary value.
Ghulam Qadir, Posdoctoral fellow, Computational Statistics group at Heidelberg Institute for Theoretical Studies, Germany
Thursday, November 17, 2022, 10:00
- 11:00
Building 1, Level 4, Room 4102
Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.
Monday, May 31, 2021, 16:00
- 18:00
The modeling of spatio-temporal and multivariate spatial random fields has been an important and growing area of research due to the increasing availability of space-time-referenced data in a large number of scientific applications. In geostatistics, the covariance function plays a crucial role in describing the spatio-temporal dependence in the data and is key to statistical modeling, inference, stochastic simulation, and prediction. Therefore, the development of flexible covariance models, which can accommodate the inherent variability of the real data, is necessary for advantageous modeling of random fields. This thesis is composed of four significant contributions in the development and applications of new covariance models for stationary multivariate spatial processes, and nonstationary spatial and spatio-temporal processes. Firstly, this thesis proposes a semiparametric approach for multivariate covariance function estimation with flexible specification of the cross-covariance functions via their spectral representations. The flexibility in the proposed cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows for capturing non-trivial cross-spectral features. The proposed method is applied to model and predict the bivariate data of particulate matter concentration and wind speed in the United States. Secondly, this thesis introduces a parametric class of multivariate covariance functions with asymmetric cross-covariance functions. The proposed covariance model is applied to analyze the asymmetry and perform prediction in a trivariate data of particulate matter concentration, wind speed and relative humidity in the United States.
 Thirdly, the thesis presents a space deformation method which imparts nonstationarity to any stationary covariance function. The proposed method utilizes the functional data registration algorithm and classical multidimensional scaling to estimate the spatial deformation. The application of the proposed method is demonstrated on precipitation data from Colorado, United States. Finally, this thesis proposes a parametric class of time-varying spatio-temporal covariance functions, which are stationary in space but nonstationary in time. The proposed time-varying spatio-temporal covariance model is applied to study the seasonality effect and perform space-time predictions in the daily particulate matter concentration data from Oregon, United States.
Monday, November 09, 2020, 16:00
- 18:00
This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions.
Monday, November 02, 2020, 16:00
- 18:00
Environmental statistics plays an important role in many related applications, such as weather-related risk assessment for urban design and crop growth. However, modeling the spatio-temporal dynamics of environmental data is challenging due to their inherent high variability and nonstationarity. This dissertation focuses on the modeling, simulation, and prediction of spatio-temporal processes using statistical techniques and machine learning algorithms, especially for nonstationary processes.
Monday, November 18, 2019, 00:00
- 23:45
Auditorium 0215, between building 2 and 3
2019 Statistics and Data Science Workshop confirmed speakers include Prof. Alexander Aue, University of California Davis, USA, Prof. Francois Bachoc, University Toulouse 3, France, Prof. Rosa M. Crujeiras Casais, University of Santiago de Compostela, Spain, Prof. Emanuele Giorgi, Lancaster University, UK, Prof. Jeremy Heng, ESSEC Asia-Pacific, Singapore, Prof. Birgir Hrafnkelsson, University of Iceland, Iceland, Prof. Ajay Jasra, KAUST, Saudi Arabia, Prof. Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project, Japan, Prof. Robert Krafty, University of Pittsburgh, USA, Prof. Guido Kuersteiner, University of Maryland, USA, Prof. Paula Moraga, University of Bath, UK, Prof. Tadeusz Patzek, KAUST, Saudi Arabia, Prof. Brian Reich, North Carolina State University, USA, Prof. Dag Tjostheim, University Bergen, Norway, Prof. Xiangliang Zhang, KAUST, Saudi Arabia, Sylvia Rose Esterby, University of British Colombia, Canada, Prof. Abdel El-Shaarawi, Retired Professor at the National Water Research Institute, Canada. View Workshop schedule and abstracts here.
Dr. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA
Tuesday, November 05, 2019, 14:00
- 15:00
Building 1, Level 4, Room 4102
In this talk, we explore a graphical model representation for the stochastic coefficients relying on the specification of the sparse precision matrix. Sparsity is encouraged in an L1-penalized likelihood framework. Estimation exploits a majorization-minimization approach. The result is a flexible nonstationary spatial model that is adaptable to very large datasets.
Tuesday, June 13, 2017, 09:00
- 10:00
B1 L4 Room 4102
In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.
Matthew Pratola, Assistant Professor of Statistics, The Ohio State University
Sunday, May 07, 2017, 16:00
- 17:00
B1 L4 room 4102
Bayesian additive regression trees (BART) has become increasingly popular as a flexible and scalable non-parametric model useful in many modern applied statistics regression problems. It brings many advantages to the practitioner dealing with large and complex non-linear response surfaces, such as a matrix-free formulation and the lack of a requirement to specify a regression basis a priori.
Dorit Hammerling, National Center for Atmospheric Research (NCAR)
Wednesday, February 10, 2016, 15:30
- 16:30
B1 L4 Room 4102
With data of rapidly increasing sizes in the environmental and geosciences such as satellite observations and high-resolution climate model runs, the spatial statistics community has recently focused on methods that are applicable to very large data. One such state-of-the-art method is the multi-resolution approximation (MRA), which was specifically developed with high performance computer architecture in mind.
William Kleiber, Assistant Professor, University of Colorado
Monday, November 09, 2015, 15:00
- 16:30
B1 L4 Room 4102
Spatial analyses often focus on spatial smoothing using the geostatistical technique known as kriging.  Theoretical results regarding large sample convergence rates of kriging predictors remain elusive.  By casting kriging as a variational problem, we develop an equivalent kernel approximation technique that can also lead to computational feasibility for large data problems.
Xiaohui Chang, Assistant Professor, College of Business at Oregon State University
Thursday, September 10, 2015, 14:30
- 16:00
B1 L4 Room 4102
We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.
Mehdi Moodaaliat, Assistant Professor, Marquette University
Tuesday, March 10, 2015, 15:00
- 16:00
In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.
Matthew Pratola, Assistant Professor of Statistics, The Ohio State University
Monday, November 24, 2014, 15:00
- 16:00
B1 East Side L2 MPR
In this talk, we introduce 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.
Serge Guillas, Professor of Statistics, University College London (UCL)
Monday, September 08, 2014, 15:00
- 16:00
B1 East Side MPR
In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.