Statistics Family Picnic

YUFENG LIU  

 

Department of Statistics and Operations Research
University of North Carolina at Chapel Hill

 

Sparse Regression for Block Missing Data Without Imputation

 

ABSTRACT

Supervised learning techniques have been widely used in diverse scientific disciplines such as business, finance, biology and neuroscience. In this talk, I will present a new technique for flexible learning of data with complex block-missing structure. We focus on data with multiple modalities (sources or types). In practice, it is common to have block-missing structure for such multi-modality data. A new technique effectively using all available data information without imputation will be discussed. Applications for the Alzheimer's Disease Neuroimaging Initiative (ADNI) data will be used to illustrate the performance of the proposed method.

 

 

Friday, 10/19/2018, BLOC 113, 11:30 AM

THOMAS SANTNER 

 

Department of Statistics
Ohio State University 

 

Variable Selection for Deterministic Computer Simulator Output

 

 

ABSTRACT

This talk will describe a variable selection methodology to identify the “active” inputs of a deterministic simulator code. While the basic methodology can be used in many settings, the description here will use an enhancement of a Bayesian composite Gaussian Process (BCGP) model. The basic BCGP model assumes that the simulator output can be described as the sum of draw from a GP that is meant to describe the long-range mean of the output plus a draw from an independent GP that describes small-scale deviations from the long-range mean. A prior is placed on the model parameters that insures the process describing the mean is smoother than that describing local deviations from the mean. In this talk, the correlation function is assumed to be a Gaussian correlation function. Based on a Bayesian fit to an enhancement of the basic BCGP model the inputs having smaller estimated correlation parameters are judged to be more active. A reference inactive input is added to the data to judge the size of the correlation parameter for inactive inputs. The importance of design is indicated.

Joint work with Casey Davis and Christopher Hans.

 

Joint seminar with the Department of Epidemiology and Biostatistics, TAMU School of Public Health
Friday, 10/26/18, BLOC 113, 11:30 AM

CHRISTIAN MUELLER 

 

Center for Computational Biology 
Flatiron Institute, Simons Foundation 

 

Title and abstract forthcoming.

 

 

Friday, 11/2/18, BLOC 113, 11:30 AM