Texas A&M - Labors of Lab
This installment of "Labors of Lab" features statistics Ph.D candidate Richard Payne '17, who is using Bayesian statistics -- specifically big-data classification methods -- in collaboration with Texas A&M distinguished professor of statistics Bani Mallick to make algorithms more efficient.
Texas A&M statistician Mikyoung Jun has been selected to receive the American Statistical Association's 2015 ENVR Young Investigator Award, presented by the ASA Section on Statistics and the Environment (ENVR) in honor of her outstanding contributions to statistical models and methods for geophysical applications.Read More →
Our latest Labors of Lab installment features statistics Ph.D. candidate Richard Payne '17, who is using Bayesian statistics -- specifically big-data classification methods -- in collaboration with Texas A&M distinguished professor of statistics Bani Mallick to make algorithms more efficient.Read More →
10:00 AM / 11:00 AM Blocker Building, Room 447 979-845-3141
PhD Candidate, Department of Statistics
Texas A&M University
"Adaptive Basis Sampling for Smoothing Splines"
Smoothing splines provide flexible nonparametric regression estimators. Penalized likelihood method is adopted when responses are from exponential families and multivariate models are constructed with certain ANOVA decompositions. However, the high computational cost of smoothing splines for large data sets has hindered their wide application. We develop a new method, named adaptive basis sampling, for efficient computation of smoothing splines in super-large samples. Generally, a smoothing spline for a regression problem with sample size n can be expressed as a linear combination of n basis functions and its computational complexity is cubic n. We achieve a more scalable computation in the multivariate case by evaluating the smoothing spline using a smaller set of basis functions, obtained by an adaptive sampling scheme that uses values of the response variable. Our asymptotic analysis shows that smoothing splines computed via adaptive basis sampling converge to the true function at the same rate as full basis smoothing splines. We show that the proposed method outperforms a sampling method that does not use the values of response variable by simulation studies, and apply it to joint modeling of multiple sequencing samples.
All Day Blocker Building, Room 448, daily 979-845-3143
AP Statistics Summer Institute - July 6-9, 2015
This course is designed primarily for new teachers in AP Statistics. However, experienced teachers will find many useful things to take back to their classrooms. Much of the week will be devoted to experimental design, correlation and regression, probability models, and inference. We will discuss how to organize the course and how to use technology, simulation and data-gathering activities. Time will be spent working with activities and simulations as a building block for the course. At least one morning will be allocated for working on laptops to view and use online resources and applets that are immensely useful for the course.
In addition to the binder for the week, participants will receive a flash drive, which will be loaded with lessons, worksheets/handouts, class note sheets, a set of old tests, AP practice sets, projects, and activities. Participants will review the 2015 test through the questions, rubrics and the grading of student responses.
The registration fee ($475) includes classroom materials, light lunches and snacks each day.
Consultant: Michael Legacy
Dr. James H Matis (c/o Elaine James)
Department of Statistics
College Station, TX 77843-3143
PH: (979)845-3143, FX: (979)845-3144
Full refunds will be available for cancellations made by June 15, 2015. Partial refunds may be available for cancellations up to one week before the institute begin. No refunds will be made after that time.