Los Alamos National Lab
Authors: Brian J. Williams (LANL), Allison Lewis (Johns Hopkins University), Ralph C. Smith (NCSU), Max Morris (ISU), Bassam Khuwaileh (Univ. of Sharjah)
The University of Georgia
Random effects models play an important role in model-based small area estimation. Random effects account for any lack of fit of a regression model for the population means of small areas on a set of explanatory variables. In Datta, Hall and Mandal (2011, JASA), we showed that if the random effects can be dispensed with through a statistical test, then the model parameters and the small area means can be estimated substantially accurately. This work is most useful when the number of small areas, m, is moderately large.
University of California, Los Angeles
The R-squared statistic, or coefficient of determination, is commonly used to measure the predictive power of a linear model. It is interpreted as the fraction of variation in the response explained by the predictors. Despite its popularity, a direct equivalent measure is not available for nonlinear regression models and for right-censored time-to-event data. In this talk, I will show that in addition to a measure of explained variation, another measure of explained prediction error is required to assess the predictive power of a nonlinear model.
University of Michigan
We consider generalized linear regression with left-censored covariate due to the lower limit of detection. The complete case analysis by eliminating observations with values below limit of detection yields valid estimates for regression coefficients, but loses efficiency. Substitution methods are biased; and maximum likelihood method relies on parametric models for the unobservable tail probability, thus may suffer from model misspecification.
Wells Fargo and University of Michigan
Data-based decision making has always been a fundamental part of banking and finance. This has become even more so after the 2008 crisis and the heightened regulatory environment. In this presentation, I will describe the role of statistics in risk modeling and management in large banks, covering model development and model assessment. The talk will give a glimpse into different types of data structures, computing/data platforms used for big data, types of models, and how they are developed and used.
Colorado State University
In this talk, we consider two types of data from neuroscience: neuromorphology data and neuron activity data. First, we focus on data extracted from brain neuron cells of rodents and model each neuron as a data object with topological and geometric properties characterizing the branching structure, connectedness and orientation of a neuron. We define the notions of topological and geometric medians as well as quantiles based on newly-developed curve representations.
Eli Lilly and Company
Monitoring the control and capability of process parameters is a continual and mammoth task for today’s manufacturers. The importance of simple, efficient, and automated approaches cannot be overstated. Paramount in this endeavor is the determination of extreme quantiles. I will review approaches for determining these quantile from the last 25 years of literature, as well as current usage at Eli Lilly and Company. A number of candidate approaches will be carried forward into a simulation to look at their performance against a variety of distributions.
In this talk, I will present statistical issues and challenges that I have encountered in my biomedical collaborative studies of item selection in disease screening, comparison and identification of biomarkers that are more informative to disease diagnosis, and estimation of weights on relatively importance of exposure variables on health outcome. After a discussion on the issues and challenges with real examples, I will review available statistical methods and present our newly developed methods.
University of California, San Diego
We study a stylized multiple testing problem where the test statistics are independent and assumed to have the same distribution under their respective null hypotheses. We first show that, in the normal means model where the test statistics are normal Z-scores, the well-known method of (Benjamini and Hochberg, 1995) is optimal in some asymptotic sense. We then show that this is also the case of a recent distribution-free method proposed by Foygel-Barber and Candes (2015).