April 8, 2013 Time: 2:00 - 4:00 Location: Parker 362
Dr. Peng Zeng, speaker
Title: Degrees of freedom in linear regression
Abstract: In this talk, we will review the existing results on degrees of freedom and discuss its application in linear regression.
April 1, 2013 Time: 2:00 - 4:00 Location: Parker 362
Dr Ash Abebe, speaker
Title: On nonlinear signed-rank estimation
Abstract: I will discuss robust signed-rank estimation of the parameters of a general nonlinear regression model. Robustness is achieved by down-weighting in gradient space. I will briefly discuss extensions to harmonic type models used in texture modeling and signal processing.
This is based on joint work with current student Brice Merlin and former student Huybrechts Bindele.
March 25, 2013 Time: 2:00 - 4:00 Location: Parker 362
Dr Guanqun Cao will discuss
Guo, Zhou, Huang, and Hardle (2013). Functional data analysis of generalized quantile regression
PDF version of the lecture notes is available at http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2013-001.pdf
March 18, 2013 Time: 2:00 - 4:00 Location: Parker 362
February 25, 2013 Time: 2:00 - 4:00 Location: Parker 362.
Dr Guanqun Cao will discuss the following paper.
I. Epifanio and N. Ventura-Campos (2011).
Functional data analysis in shape analysis.
Computational Statistics and Data Analysis, Vol 55, pp. 2758--2773.
February 18, 2013 Time: 2:00 - 4:00 Location: Parker 362
Dr Ash Abebe will talk about a joint work with Parminder Singh: Testing for Certain Patterns in Exponential Location Parameters
Abstract: Suppose that k treatments under comparison are ordered in a certain way. For example, the treatments may be increasing dose levels in dose response experiments. The exponential distribution is generally used to model the effective duration of a drug, where the location parameter is referred to as the latency period that may decrease/increase with increase in dose. I will discuss exact tests for detecting such patterns. I will present a simple recursive integration method that can be used for computing the critical constants. This recursive method is a variation of the ones described in the following two papers by AJ Hayter:
Recursive integration methodologies with statistical applications. J. Statist. Plann. Inference 136 (2006), 7, 2284 -2296.
Recursive integration methodologies with applications to the evaluation of multivariate normal probabilities. J. Stat. Theory Pract. 5 (2011), 4, 563-589.
February 11, 2013 Time: 2:00 - 4:00pm Location: Parker 362
Dr Nedret Billor will talk about an ongoing research in collaboration with Seokho and Hyejin.
Title: M-type Smoothing Spline Estimators for Principal Functions
Abstract: We propose a robust method for estimating principal functions based on MM estimation. Specifically, we formulate functional principal component analysis into alternating penalized M-regression with a bounded loss function. The resulting principal functions are given as M-type smoothing spline estimators. Using the properties of a natural cubic spline, we develop a fast computation algorithm even for long and dense functional data. The proposed method is efficient in that the maximal information from whole observed curve is retained since it partly downweighs abnormally observed individual measurements in a single curve rather than removing or downweighing a whole curve. We demonstrate the performance of the proposed method on simulated and lip movement data and compare it with the conventional functional principal component analysis and other robust functional principal component analysis techniques.
February 04, 2013 Time: 2:00 - 4:00pm Location: Parker 362
Dr. Peng Zeng will discuss Li, Chun, and Zhao (2012). Sparse estimation of conditional graphical models with application to gene networks. Journal of the American Statistical Association, Vol 107, pp. 152--167.
A pdf version of the paper is available at the following link (from an on-campus IP address) http://www.tandfonline.com/doi/pdf/10.1080/01621459.2011.644498
January 28, 2013 Time: 2:00 - 4:00pm Location: Parker 362
Dr Xiaoyu Li will discuss A. Montanari (2011). Lecture Notes for Inference in Graphical Models.
You can find the pdf version of the lecture notes on the following website. http://www.auburn.edu/~zengpen/seminar/