Jane-Ling Wang

Modeling Left-truncated and Right Censored Survival data with longitudinal covariates

In this talk, we explore the modeling of survival data in the presence of longitudinal covariates. In particular, we consider survival data that are subject to both left truncation and right censoring. It is well known that traditional approaches, such as the partial likelihood approach for the Cox proportional hazards model encounter difficulties when longitudinal covariates are involved in the modeling of the survival data. A joint likelihood approach has been shown in the literature to provide an effective way to overcome those difficulties for right censored data.

Thursday, November 17, 2011 - 3:30pm

Hyonho Chun

Network Inference with genomic data

Current genomics research indicates that statistical analysis based on individual genes may incur loss of information on the biological process under study. Better results can be derived from the analysis based on groups of genes, or gene networks. An informative characterization of a gene network is by the global Markov property, which can be inferred by the Gaussian graphical models (GGMs). In this talk, I will present two recent network inference problems with genomics data.

Thursday, November 10, 2011 - 3:30pm

Yi Li

A New Class of Estimating Equation-based Variable Selectors for Risk Assessment

We propose a new class of estimating equation-based Dantzig selectors that can achieve simultaneous estimation and variable selection in the absence of a likelihood function, even when the number of covariates exceeds the number of samples. Our research was motivated by practical problems encountered in two studies: a clinical trial of therapies for head and neck cancer, and a genomics study of multiple myeloma patients. These problems proved difficult to analyze under the likelihood setting and must instead be approached with estimating equations.

Thursday, November 3, 2011 - 3:30pm

Xiaobo Ding

University of Washington

Constrained least square estimator of the transformation function for a partially linear single-index transformation model

We propose a constrained least square estimator of the transformation function of a partially linear single-index transformation model, where the transformation function, single-index function and error distribution are all nonparametric. The estimators of the regression coefficients and the single-index function are provided by the similar idea to the minimum average variance estimation method. Basis function approximation is employed to estimate the transformation and single-index functions, and cross validation criteria are proposed to select suitable sets of basis functions.

Thursday, October 27, 2011 - 3:30pm

Warren Kuhfeld

SAS Institute

The Design of Experiments for Stated Choice Models

Marketing, transportation, environmental, and other researchers need to understand how people make choices. Researchers design experiments, collect data, and fit models to understand people’s preferences. This talk will explain some commonly used methods for designing choice experiments along with a series of SAS tools that you can use to design and evaluating choice experiments. Design methods include generic and alternative-specific choice designs, partial profiles, and MaxDiff designs. Building blocks include orthogonal arrays and balanced incomplete block designs.

Thursday, October 20, 2011 - 3:30pm

Haiyan Cai

National Science Foundation

Some Properties of Random Networks

I will talk briefly some of my recent research on random networks. In the first part of the talk, we will focus on the connectivity of a random network. The network is formed from a set of randomly located points and their connections depend on the distance between the points. It is clear that the probability of connection depends on the density of the points. We will explore some properties of this probability as a function of the point density. In the second part, I will discuss a possible approach in the study correlation structure of a large number of random variables.

Wednesday, October 5, 2011 - 2:30pm

Mi-Ok Kim

Semiparametric Approach to a Random Effects Quantile Regression Model

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest, while the random coefficients represent cluster specific deviations in the covariate effects.

Thursday, September 29, 2011 - 3:30pm

Lianming Wang

Bayesian semiparametric regression analysis of interval-censored data with monotone splines

Interval-censored data naturally arise in many fields such as aids clinical trial studies and follow-up medical studies. The main feature is that the failure time of interest is not observed exactly but is known to fall within some interval. Regression analysis on interval-censored data is challenging due to the complex data likelihood and the censoring mechanism producing such data. In this talk, I will review the commonly used semiparametric regression models and the existing methods in the literature and will focus on our newly proposed Bayesian methods under several regression models.

Thursday, September 22, 2011 - 3:30pm

Ana-Maria Staicu

Skewed functional processes and their applications

We introduce a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial location. Such data are not envisaged by the current approaches to model functional data, due to the lack of Gaussian – like features. Our methodology allows modeling the pointwise quantiles, has interpretability advantages and is computationally feasible. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls.

Thursday, September 15, 2011 - 3:30pm

MoonJung Cho

Bureau of Labor Statistics

Evaluation of Generalized Variance Function Estimators for the U.S. Current Employment Survey

In applied work with generalized variance function models for sample survey data, one generally seeks to develop and validate a model that is relatively parsimonious and that produces variance estimators that are approximately unbiased and relatively stable. This development and validation work often begins with regression of initial variance estimators (computed through standard design-based methods) on one or more candidate explanatory variables. Evaluation of initial modeling results is often complicated by correlation among the initial variance estimators.

Thursday, September 8, 2011 - 3:30pm


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