In this talk, I will first present a method for conditional distribution and quantile estimation when predictors take values in a functional space, which is an extension of the usual functional mean regression. The study is motivated and illustrated by an application to the assessment of children’s growth patterns. The proposed method is supported by theory and is shown to perform well in simulations. An extension of the proposed conditional approach to model the more complex case when responses are also functions will be briefly discussed.
The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.
Type of Event:
We introduce a flexible inferential framework for the longitudinal analysis of ultra high dimensional data. Typical examples of such data structures include, but are not limited to, observational studies that collect imaging data longitudinally on large cohorts of subjects.
Statistics 311 at NCSU is a large introductory course that serves over 700 students per semester. In this talk I will present the results of a redesign that transformed this course in a hybrid format. This project converted two of the three hours of student contact to an online format. Students still come together one day a week to participate in discussions where an instructor teaches them the important ideas and concepts. During this time students also participate in group activities that illustrate key ideas.
In oncology trials with time to event as primary endpoints, the trial duration is mainly driven by the number of events observed to ensure sufficient power to draw confirmatory conclusions. The trial planning determined by clinical staff and the deterministic techniques fails to account for the uncertainties and stochastic fluctuations in the recruitment process and event evolvement. In this project, we incorporate a Poisson-gamma recruitment process into an exponential event prediction model under the empirical Bayesian setting.
This presentation begins with a behind-the-scenes look at how research statistician developers at SAS interact with customers and the statistical community to decide on new functionality in SAS/STAT. The presentation then provides a high-level tour of new directions and features in the most recent releases of SAS/STAT (9.2, 9.22, and 9.3).
In this talk, we present maximum empirical likelihood estimation in the case of constraint functions that may be discontinuous and/or depend on additional parameters. The key to our analysis is a uniform local asymptotic normality condition for the local empirical likelihood ratio. This condition holds under mild assumptions and allows for a study of maximum empirical likelihood estimation and empirical likelihood ratio testing similar to that for parametric models.
We present research on clinical trials with a sensitive subpopulation of patients, that is, a subgroup that is more likely to benefit from the treatment than the overall population. Given a sensitive subgroup defined by a prespecified classifier, for example, a clinical marker or pharmacogenomic marker, the trial’s outcome is declared positive if the treatment effect is established in the overall population or in the subgroup.
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.
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.
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.