MINOR REQUIREMENTS
The Department of Statistics offers a minor in Statistics. To declare a minor in Statistics, please meet with a Faculty Advisor. You can make an appointment here. For any additional questions, please contact undergraduate@stat.tamu.edu. Note: the prerequisite for STAT 211 is MATH 152 or MATH 172.
| Lower Division Courses | ||
| STAT 211 | Principles of Statistics I | 3 |
| STAT 212 | Principles of Statistics II | 3 |
| Upper Division Electives | ||
| Select three of the following: 1 | ||
| Statistical Computing | 3 | |
| Design and Analysis of Experiments | 3 | |
| Principles of Survey Samplings | 3 | |
| Introduction to Linear Models | 3 | |
| Mathematical Statistics I | 3 | |
| Mathematical Statistics II | 3 | |
| Methods in Time Series Analysis | 3 | |
| Multivariate Analysis and Statistical Learning | 3 | |
| Bayesian Statistics | 3 | |
| Applied Biostatistics and Data Analysis | 3 | |
| Statistical Bioinformatics | 3 | |
| Categorical Data Analysis | 3 | |
| Directed Studies | 3 | |
| Special Topics in… | 3 | |
Additional Requirements:
Students must make a grade of “C” or better in all courses.
Substitutions for the minor must be approved by the Statistics Department.
StatLinks
Calendar
October 2018
Departmental Colloquia: Yufeng Liu
October 19, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
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
Departmental Colloquia: Thomas Santner
October 26, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
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
Departmental Colloquia: Christian Mueller
November 2, 201811:30 AM / 12:30 PM Blocker Building (BLOC), Room 113 979-845-3141
