Category Archives: Department of Statistics

2015 Illinois Statistics Symposium – A Celebration of the Department’s 30th Anniversary

2015 Illinois Statistics Symposium: A Celebration of the Department’s 30th Anniversary

 

 

The symposium offers our alumni a great opportunity to return to the University of Illinois to celebrate the 30th anniversary of the Statistics Department and to meet with many of their classmates, professors, and colleagues in a warm and friendly setting.

 

 

Date: Saturday, November 21, 2015

Time: All day, beginning at 9:00 AM

Location: I-Hotel & Conference Center, Lincoln Room

Sponsor: Department of Statistics

 

 

For more information, including a detailed agenda, please click the link below to view this event on our calendar.

 

 

http://illinois.edu/calendar/detail/1439?eventId=33033381

 

Undergraduate Research Apprentice Program now accepting applications

Undergraduate Research Apprentice Program (Pilot; Spring 2016)

The Office of Undergraduate Research (OUR) and the Graduate College are partnering to offer an opportunity for first and second-year undergraduate students to gain undergraduate research experience while working under the direct supervision of advanced graduate students. Beginning this Spring 2016, we will provide paid apprenticeships to ten selected undergraduate students.  Undergraduate apprentices will have an opportunity to conduct and learn the research process through a one-on-one research experience with their graduate student mentor. Students in the humanities, social sciences, and fine and applied arts are particularly encouraged to apply; however, we will accept applications from students in all disciplines. Please see the blog on OUR’s webpage – www.undergradresearch.illinois.edu – for more information and to apply. Deadline for application is Friday, November 20, 2015.

 

Questions may be addressed to: ugresearch@illinois.edu; please put “URAP” in the subject line.

 

Office of Undergraduate Research

Fifth Floor Illini Union Bookstore

807 S. Wright Street, M/C 317

Champaign, IL 61820

Office phone: (217) 300-5453

ugresearch@illinois.edu

www.undergradresearch.illinois.edu

Statistics Seminar

“Breaking Data Science”

Dr. Robert Brunner, University of Illinois at Urbana-Champaign

 

Date: Thursday, October 22, 2015

Time: 3:30 PM – 4:30 PM

Location: 269 Everitt

Sponsor: Department of Statistics

 

Abstract:

We live in an increasingly digital world, where an ever-growing quantity of information is generated, collected, and archived. The transformation we are witnessing produces new opportunities across many seemingly unrelated academic fields, leading to the development and growth of a new field: Data Science. In this seminar, I will briefly introduce this new field before reviewing my personal transformation into a Data Scientist. I will conclude with a brief discussion on what I feel the University of Illinois should do to build a data science program that will position us as international leaders in this new field.

 

http://illinois.edu/calendar/detail/1439?eventId=32978618&calMin=201510&cal=20151019&skinId=13335

Statistics Seminar – Dr. Hyokyoung Hong – Thursday, October 08, 2015

 

“A data-driven approach to conditional screening of high dimensional variables”

Dr. Hyokyoung Hong, Michigan State University

 

Date: Thursday, October 08, 2015

Time: 3:30 PM – 4:30 PM

Location: 269 Everitt

Sponsor: Department of Statistics

 

Abstract:

Marginal screening is a widely applied technique to handily reduce the dimensionality of the data when the number of potential features overwhelms the sample size. Due to the nature of the marginal screening procedures, they are also known for their difficulty in identifying the so- called hidden variables that are jointly important but have weak marginal associations with the response variable. Failing to include a hidden variable in the screening stage has two undesirable consequences: (1) important features are missed out in model selection; and (2) biased inference is likely to occur in the subsequent analysis. Motivated by some recent work in conditional screening, we propose a data-driven conditional screening algorithm, which is computationally efficient, enjoys the sure screening property under weaker assumptions on the model, and works robustly in a variety of settings to reduce false negatives of hidden variables. Numerical comparison with alternatives screening procedures are also made to shed light on the relative merit of the proposed method. We illustrate the proposed methodology using a leukemia microarray data example.

 

http://illinois.edu/calendar/detail/1439?eventId=32932610&calMin=201510&cal=20151005&skinId=13335

 

Statistics Seminar upcoming

 

Statistics Seminar

 

“Variance estimation for high-dimensional linear models: Fixed-effects, random-effects, and quadratic forms”

Dr. Lee Dicker, Rutgers University

 

Date: Thursday, September 24, 2015

Time: 3:30 PM – 4:30 PM

Location: 269 Everitt

Sponsor: Department of Statistics

 

Abstract:

In regression analysis with repeated measurements, such as longitudinal data and panel data, structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the repeated measurements (p) is smaller than the sample size (n). However, the assessment becomes challenging when p is bigger than n, since the classical likelihood-ratio test is no longer applicable. This talk will focus an adjusted goodness-of-fit test, which is designed to examine a broad range of covariance structures under the scenario of “large p, small n”. The analytical examples will be presented to illustrate the effectiveness of adjustment for assessing the goodness-of-fit of covariance. In addition, large sample properties of the proposed test are established. Moreover, simulation studies and a real data example are provided to demonstrate the finite sample performance and the practical utility of the test.

 

http://illinois.edu/calendar/detail/1439?eventId=32825570&calMin=201509&cal=20150921&skinId=13335

Statistics Seminar – Thursday, September 17, 2015

Statistics Seminar

 

“Tests for Covariance Structures with High-dimensional Repeated Measurements”

Dr. Ping-Shou Zhong, Michigan State University

 

Date: Thursday, September 17, 2015

Time: 3:30 PM – 4:30 PM

Location: 269 Everitt

Sponsor: Department of Statistics

 

Abstract:

In regression analysis with repeated measurements, such as longitudinal data and panel data, structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the repeated measurements (p) is smaller than the sample size (n). However, the assessment becomes challenging when p is bigger than n, since the classical likelihood-ratio test is no longer applicable. This talk will focus an adjusted goodness-of-fit test, which is designed to examine a broad range of covariance structures under the scenario of “large p, small n”. The analytical examples will be presented to illustrate the effectiveness of adjustment for assessing the goodness-of-fit of covariance. In addition, large sample properties of the proposed test are established. Moreover, simulation studies and a real data example are provided to demonstrate the finite sample performance and the practical utility of the test.

 

http://illinois.edu/calendar/detail/1439?eventId=32511447&calMin=201509&cal=20150914&skinId=13335

 

Statistics Seminar with Stephen Portnoy

A talk giving a gentle introduction to Regression Quantiles
will by given by  Steve Portnoy: Wed, Sep 16, 2015 3pm ‐ 4 plus,
Henry Administration Building, Room 138. If there is time, and you
want to see a couple of additional examples, I might suggest loading
the R-package “quantreg” (of Roger Koenker) and bringing your laptop.

I have a couple of undergraduates doing a reading-research course
on inference for regression quantiles with me this fall, and plan
to give a few lectures. Several statistics students (and faculty)
expressed an interest, so I have a moderately large room and welcome any people who might have heard of the topic and wondered what a regression quantile is.

– Steve Portnoy

Statistics Seminar – Thursday, September 10, 2015

“Changepoints and Associated Climate Controversies”

Robert Lund, Clemson University

 

Date: Thursday, September 10, 2015

Time: 3:30 PM – 4:30 PM

Location: 269 Everitt

Sponsor: Department of Statistics

 

Abstract:

This talk overviews changepoint issues in climatology. Changepoints (inhomogeneities) are ubiquitous features in climatic time series, arising, for example, when stations relocate or instrumentation is changed. Changepoints confound many inference problems and are very important data features. Here, we show why changepoint information is essential in making accurate trend conclusions. Examples are given where inferences are questionable when changepoints are ignored. The talk will delve into two recent contentious climate issues: 1) the recent increase in Atlantic Basin hurricanes; and 2) the “warming hole” (lack of warming) seen in the Eastern United States.

 

http://illinois.edu/calendar/detail/1439?eventId=32777743&calMin=201509&cal=20150908&skinId=13335