Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Yongdai Kim

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…


Pritam Ranjan

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-…


Yichuan Zhao

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…


Ian Dryden

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…


Yufeng Liu

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…


Limin Peng

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…


Bin Wang

Microarray technology has been used to measure the messenger RNA (mRNA) expression in gene expression profiling studies. In recent years, this technology has been applied in microRNA (miRNA) discovery. In this study, we profile miRNAs from a panel of osteosarcoma xenografts individually using LNA microarray, beads-based array and TLDA cards, and compare the consistency of these three platforms. Several new miRNA normalization methods will also…


Danny Pfeffermann

We develop a new method for bias correction of correct order, which models the error of the target estimator as a function of the corresponding bootstrap estimator, and the original estimators and bootstrap estimators when estimating the parameters governing the model underlying the sample. This is achieved by considering a large set of plausible parameter values, generating pseudo original samples and bootstrap samples for each parameter and…


Kevin Dobbin

There are now several methods for constructing confidence intervals for prediction accuracy in high dimensional settings. But these methods have high computational cost and are cumbersome to implement. As a result, these types of intervals are rarely reported, and their properties are not well understood. In this talk, we review these methods, one in some detail, and introduce current work which utilizes a mathematical modeling approach to try…


Hua Liang

We are concerned with how to select significant variables in semi-parametric modeling. Variable selection for semi-parametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for parametric portion. Thus, it is much more challenging than that for parametric models such as linear models and generalized linear models because traditional variable selection procedures…
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