Greg Rempala

Statistical Inference for Markov Jump Processes and Stochastic Epidemic Models
The presentation shall describe some conditions on the data process and the underlying likelihood function which guarantee the identifiability of the process parameters and the consistency of the maximum likelihood estimates.

The theory of Markov jump processes has broad applications in molecular biology and population dynamics modeling. One of the most important practical aspects of analyzing models based on Markov jump processes under the so called “mass-action" kinetics, is the inference on the reaction rate constants. The presentation shall describe some conditions on the data process and the underlying likelihood function which guarantee the identifiability of the process parameters and the consistency of the maximum likelihood estimates.

Thursday, September 23, 2010 - 3:30pm
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Dennis D. Boos

Variable Selection in Second-Order Models using the False Selection Rate (FSR) Approach

Variable selection of main effects is often a first step of model building followed by consideration of interactions and nonlinearities. We consider selection of second-order models under various hierarchy restrictions between main effects and second-order terms (squares and interactions) using the FSR approach of Wu et al. (2007, JASA) and Boos et al. (2009, Biometrics). The basic idea is to control the proportion of uninformative variables in the final model.

Thursday, September 16, 2010 - 3:30pm
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Hui Zou

Non-concave Penalized Composite Likelihood Estimation of Sparse Ising Models

The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging especially in the presence of high dimensional parameters. In this work, we propose efficient procedures for learning a sparse Ising model based on a penalized composite likelihood with non-concave penalties.

Thursday, September 2, 2010 - 3:30pm
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Yongdai Kim

Seoul National University, South Korea

Sparse Regression with Incentive

Spare regularization methods for high dimensional regression have received much attention recently as an alternative of subset selection methods. Examples are lasso (Tibshirani 1996), bridge regression (1993), scad (2001), to name just few. An advantage of sparse regularization methods is that it gives a stable estimator with automatic variable selection and hence the resulting estimator performs well in prediction. Also, sparse regularization methods have many desirable properties when the true model is sparse.

Thursday, August 26, 2010 - 3:30pm
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Pritam Ranjan

Tikhonov Regularization for Emulating Deterministic Computer Simulators

For many expensive computer simulators, the outputs are deterministic and thus the desired statistical surrogate (emulator) is an interpolator of the observed data. Gaussian spatial process (GP) is commonly used to model such simulator outputs. Fitting a GP model to n data points requires numerous inversion of a correlation matrix R. This becomes computationally unstable due to near-singularity of R. The popular approach to overcome near-singularity introduces over-smoothing of the data.

Thursday, August 19, 2010 - 3:30pm
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Yichuan Zhao

Empirical Likelihood Intervals for the Difference of Two Quantile Functions with Right Censoring

In this talk, we study two independent samples under right censoring. Using a smoothed empirical likelihood method, we investigate the difference of quantiles in the two samples and construct the pointwise confidence intervals from it as well. The empirical log-likelihood ratio is proposed and its asymptotic limit is shown as a standard chi-squared distribution. In the simulation studies, we compare the empirical likelihood method and the normal approximation method in terms of coverage accuracy and average length of confidence intervals.

Thursday, October 8, 2009 - 3:30pm
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Ian Dryden

University of South Carolina

Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging

The statistical analysis of covariance matrices occurs in many important applications, e.g. in diffusion tensor imaging or longitudinal data analysis. Methodology is discussed for estimating covariance matrices which takes into account the non-Euclidean nature of the space of positive semi-definite symmetric matrices. We make connections with the use of Procrustes methods in shape analysis, and comparisons are made with other estimation techniques, including using the matrix logarithm, Riemannian metric, matrix square root and Cholesky decomposition.

Thursday, October 1, 2009 - 3:30pm
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Yufeng Liu

University of North Carolina

Estimation of Multiple Noncrossing Quantile Regression Functions

Quantile regression is a very useful statistical tool to learn the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions.

Thursday, September 17, 2009 - 3:30pm
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Limin Peng

Emory University

Survival Analysis with Quantile Regression Models

Quantile regression offers great flexibility in assessing covariate effects on event times, thereby attracting considerable interests in its applications in survival analysis. However, currently available methods often require stringent assumptions on the censoring mechanism or residual distribution, or complex algorithms, which may complicate both theoretical arguments and inferences. In this paper we develop a new quantile regression approach for survival data subject to conditionally independent censoring.

Thursday, September 17, 2009 - 3:30pm
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